399 Lstm Jobs - Page 2

Experience: 2 To 6 Years • Industry experience in applied research in the field of deep learning and computer vision • Exposure to state of the art technologies in Deep learning (e.g. Transformers, GANs, LSTM, BEV) • Experience in Deep Learning in one or more of the following: object detection, segmentation, tracking, pose estimation, action recognition, differentiable rendering • Expert in Python programming and good understanding of deep learning frameworks and workflow • Experience in working with large data sets and developing infrastructure pipelines • Strong fundamentals in 3D geometry • Strong knowledge of Camera parameters and color models • High level of innovation and motivation • Extensive knowledge of ML frameworks, libraries, data structures, data modeling, and software architecture • In-depth knowledge of mathematics, statistics, and algorithms • Good communication and collaboration skills • Specialization in Machine Learning preferred. • Applied Deep Learning work experience preferred. • Algorithm development experience in robotics, ultrasonics, RADAR, LIDAR, camera systems, sensor fusion or computer vision • Experience with PC-based simulation tools • Knowledge of UML design tools • Academic publications in relevant field • Experience developing software for embedded platforms. • Experience with version control software • Automotive industry experience. • Experience with imaging or optics systems. • Experience developing algorithms for autonomous and/or real-time systems. • Superb analytical and problem-solving abilities

JOB DESCRIPTION Roles & responsibilities Here are some of the key responsibilities of Sr AI Research Scientist: Research and Development: Conduct original research on generative AI models, focusing on model architecture, training methodologies, fine-tuning techniques, and evaluation strategies. Maintain a strong publication record in top-tier conferences and journals, showcasing contributions to the fields of Natural Language Processing (NLP), Deep Learning (DL), and Machine Learning (ML). Experience with POCs on emerging and latest innovation in AI. Multimodal Development: Design and experiment with multimodal generative models that integrate various data types, including text, images, and other modalities to enhance AI capabilities. Agentic AI Systems: Develop and design autonomous AI systems that exhibit agentic behavior, capable of making independent decisions and adapting to dynamic environments. Design and Implementation: Lead the design, development, and implementation of generative AI models and systems, ensuring a deep understanding of the domain. Select suitable models, train them on large datasets, fine-tune hyperparameters, and optimize overall performance. Algorithm Optimization: Optimize generative AI algorithms to enhance their efficiency, scalability, and computational performance through techniques such as parallelization, distributed computing, and hardware acceleration, maximizing the capabilities of modern computing architectures. Data Preprocessing and Feature Engineering: Manage large datasets by performing data preprocessing and feature engineering to extract critical information for generative AI models. This includes tasks such as data cleaning, normalization, dimensionality reduction, and feature selection. Model Evaluation and Validation: Evaluate the performance of generative AI models using relevant metrics and validation techniques. Conduct experiments, analyze results, and iteratively refine models to meet desired performance benchmarks. Technical Leadership: Provide technical leadership and mentorship to junior team members, guiding their development in generative AI through work reviews, skill-building, and knowledge sharing. Ability to drive multiple teams and cross-collaborate to ensure the quality delivery. Documentation and Reporting: Document research findings, model architectures, methodologies, and experimental results thoroughly. Prepare technical reports, presentations, and whitepapers to effectively communicate insights and findings to stakeholders. Continuous Learning and Innovation: Stay abreast of the latest advancements in generative AI by reading research papers, attending conferences, and engaging with relevant communities. Foster a culture of learning and innovation within the team to drive continuous improvement. Mandatory technical & functional skills Machine learning frameworks - PyTorch or TensorFlow. Deep Learning algorithms - CNN, RNN, LSTM, Transformers LLMs ( BERT, GPT, etc.) and NLP algorithms. Design experience for fine Tuning of Open source LLMs from Huggingface, Meta- LLaMA 3.1, BLOOM, Mistral AI etc. GCP : Vertex AI or Azure : AI Foundry or AWS SageMaker Scientific understanding - PEFT - LORA, QLORA, etc. Exposure to GCP : Vertex AI or Azure : AI Foundry or AWS SageMaker In-depth conceptual understanding on emerging and latest innovation in AI. Stay current with AI trends - MCP, A2A protocol, ACP, etc. Preferred Technical & Functional Skills Langgraph/CrewAI/Autogen Large scale deployment of GenAI/DL/ML projects, with good understanding of MLOps /LLM Ops Ensure scalability and efficiency, handle data tasks, Cloud computing experience- Azure/AWS/GCP BigQuery/Synapse Key behavioral attributes/requirements Ability to mentor Managers and Tech Leads Ability to own project deliverables, not just individual tasks Understand business objectives and functions to support data needs RESPONSIBILITIES Roles & responsibilities Here are some of the key responsibilities of Sr AI Research Scientist: Research and Development: Conduct original research on generative AI models, focusing on model architecture, training methodologies, fine-tuning techniques, and evaluation strategies. Maintain a strong publication record in top-tier conferences and journals, showcasing contributions to the fields of Natural Language Processing (NLP), Deep Learning (DL), and Machine Learning (ML). Experience with POCs on emerging and latest innovation in AI. Multimodal Development: Design and experiment with multimodal generative models that integrate various data types, including text, images, and other modalities to enhance AI capabilities. Agentic AI Systems: Develop and design autonomous AI systems that exhibit agentic behavior, capable of making independent decisions and adapting to dynamic environments. Design and Implementation: Lead the design, development, and implementation of generative AI models and systems, ensuring a deep understanding of the domain. Select suitable models, train them on large datasets, fine-tune hyperparameters, and optimize overall performance. Algorithm Optimization: Optimize generative AI algorithms to enhance their efficiency, scalability, and computational performance through techniques such as parallelization, distributed computing, and hardware acceleration, maximizing the capabilities of modern computing architectures. Data Preprocessing and Feature Engineering: Manage large datasets by performing data preprocessing and feature engineering to extract critical information for generative AI models. This includes tasks such as data cleaning, normalization, dimensionality reduction, and feature selection. Model Evaluation and Validation: Evaluate the performance of generative AI models using relevant metrics and validation techniques. Conduct experiments, analyze results, and iteratively refine models to meet desired performance benchmarks. Technical Leadership: Provide technical leadership and mentorship to junior team members, guiding their development in generative AI through work reviews, skill-building, and knowledge sharing. Ability to drive multiple teams and cross-collaborate to ensure the quality delivery. Documentation and Reporting: Document research findings, model architectures, methodologies, and experimental results thoroughly. Prepare technical reports, presentations, and whitepapers to effectively communicate insights and findings to stakeholders. Continuous Learning and Innovation: Stay abreast of the latest advancements in generative AI by reading research papers, attending conferences, and engaging with relevant communities. Foster a culture of learning and innovation within the team to drive continuous improvement. Mandatory technical & functional skills Machine learning frameworks - PyTorch or TensorFlow. Deep Learning algorithms - CNN, RNN, LSTM, Transformers LLMs ( BERT, GPT, etc.) and NLP algorithms. Design experience for fine Tuning of Open source LLMs from Huggingface, Meta- LLaMA 3.1, BLOOM, Mistral AI etc. GCP : Vertex AI or Azure : AI Foundry or AWS SageMaker Scientific understanding - PEFT - LORA, QLORA, etc. Exposure to GCP : Vertex AI or Azure : AI Foundry or AWS SageMaker In-depth conceptual understanding on emerging and latest innovation in AI. Stay current with AI trends - MCP, A2A protocol, ACP, etc. Preferred Technical & Functional Skills Langgraph/CrewAI/Autogen Large scale deployment of GenAI/DL/ML projects, with good understanding of MLOps /LLM Ops Ensure scalability and efficiency, handle data tasks, Cloud computing experience- Azure/AWS/GCP BigQuery/Synapse Key behavioral attributes/requirements Ability to mentor Managers and Tech Leads Ability to own project deliverables, not just individual tasks Understand business objectives and functions to support data needs QUALIFICATIONS This role is for you if you have the below Educational Qualifications Masters (MS by Research)/PhD or equivalent degree in Computer Science Preferences to research scholars from Tier 1 colleges- IITs, NITs, IISc, IIITs, ISIs, etc. Work Experience 12+ Years of experience with strong record of publications (at least 5) in top tier conferences and journals #KGS Show more Show less

About Info Edge: InfoEdge's mission is to create world-class platforms that transform lives by continuously innovating. Our products and services are built keeping our customers in mind. We always delight our customers by delivering superior value through enhanced offerings on the internet and other platforms. Through our continuous investment across various businesses, especially in cutting-edge technology, machine learning and artificial intelligence (AI), we have built a robust system that constantly increases our predictive powers on customer behaviour, and optimizes and improves our systems. Our various teams tirelessly work together to solve problems, innovate, and create something to empower our customers. At Info Edge, people are our core competitive advantage and we will continue doing all that is needed to attract and retain the best available talent. * About BU: Corporate Function * Required Educational Qualification: PhD/ MTech / BTech with relevant experience from Tier 1 Campus only. * Desired Experience: 0 - 5 Years * Job Objective: You will be building innovative Machine Learning solutions for vital and complex business problems. *Fill this form to proceed further: ( https://tinyurl.com/FAIscientist ) Job Description: As AI Scientist you will: Develop algorithms and build systems to extract seemingly unseen trends and information from user content such as resumes, job description and profile information available through various sources and in different modalities such as numeric, structure and unstructured texts and images. Identify potential new business problems that can be solved through machine learning. Ideate, formulate, create metrics and execute on such problems and get buy in from business. Identify potential data-driven machine learning solutions to improve user experience through personalization and prediction of user preferences. Build intelligent systems to capture and model the vast amount of behavior data to enrich the content understanding with behavioral information. Identify the appropriate and cutting edge machine learning tools for various supervised and unsupervised tasks in the NLP, deep learning, semantic search, LTR space for building highly accurate and scalable recommendation systems and information retrieval solutions. Design solutions for scalable and real-time performance on a significantly large data set. Use big data technologies to optimally use infrastructure and improve performance. Participate in external forums /discussions, publish research papers, keep up-to-date on latest publications and build external/internal networks. Required Skills: Strong fundamental understanding and research experience in machine learning/Deep Learning/NLP/Information Retrieval/Artificial Intelligence areas. Strong applied knowledge of machine learning in solving real-world business problems with a significantly broad number of algorithms covering the landscape of classification, regression; discriminative, generative; supervised, unsupervised, semi-supervised; linear and non-linear dimensionality reduction, feature-extraction, feature selection, feature-learning. Industry experience in applying a variety of machine learning solutions to real-world large-scale data to build intelligent systems. An excellent problem solving skills with a research oriented approach. Ability to capture, manage, create pipelines and process Big Data. Desired Skills: Ability to understand Business challenges and define solutions aligned to the needs.

Job Description At American Express, our culture is built on a 175-year history of innovation, shared and Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues. As part of Team Amex, you'll experience this powerful backing with comprehensive support for your holistic well-being and many opportunities to learn new skills, develop as a leader, and grow your career. Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express. How will you make an impact in this role Function Description: With a focus on digitization, innovation, and analytics, the Global Decision Sciences (GDS) team creates central, scalable platforms and customer experiences to help markets across all of these priorities. Charter is to drive scale for the business, and accelerate innovation for both immediate impact as well as long-term transformation of our business. A unique aspect of GDS is the integration of diverse skills across all of its remit. GDS has a very broad range of responsibilities, resulting in a broad range of initiatives around the world. The Amex AI Labs of American Express Corporation is looking for best in class Senior Research Engineers to work in a dynamic and rewarding workplace. Amex AI Labs is currently co-located in Bangalore, Gurgaon and New York and involves dynamic and talented researchers with Ph.D. or Master's degree from top tier institutes in India and abroad having research and product development expertise in AI/ML technologies such as - Deep Learning, Data Science, Natural Language Processing, Cloud Technologies and related technical areas. Going beyond building products and solutions for traditional financial domains such as credit and fraud, the team is also working on challenging research problems in domains such as services, human resource management, travel & lifestyle, and many more. The key goal is to progress the state of the art in science and develop products to solve business problems, in a domain-agnostic manner, for American Express. Purpose of the Role: Collaborate on next generation research services in the areas of Artificial Intelligence (AI) to aid solutions to complex business problems for American Express Responsibilities: Amex AI Labs is looking for individuals with AI R&D background for AI Research & Services team in Bangalore/Gurgaon. The team works on handling capabilities centred on Document Analysis, NLP, AI research from conception to launch. Successful candidates are expected to partner with different business and technology teams in American Express to build capabilities for high impact use-cases. We are looking for extremely agile and entrepreneurial candidates who will help innovate and execute product initiatives across the company. . Lead the ideation and launch of innovative AI/ML capabilities & services . Hands on Deep learning Algorithms like BERT, RNN, LSTM, Transformer, GRU, GAN etc . Understanding Generative AI based closed source models like ChatGPT, Claude, Llama for different usecases . Fine Tuning open source models like Llama and other models on domain specific data . Working with MLOPs capability like MLFlow, AirFlow etc and understanding of deployment capabilities on public cloud (AWS, GCP) and on-prem infrastructure . Technically hands-on working with big data sets using Python/JAVA/SPARK, doing EDA and bringing insights from data . Building POCs basis on research papers analysis while simulating business impact and Transform MVPs to production grade capabilities in collaboration with engineering teams . Authoring and publishing papers, adopting open source tools, presenting in conferences, conducting R&D driving company revenue. . Innovate ways to evangelize the product to drive Amex wide user adoption . Contribute to the strategy and roadmap of AI/ML Initiatives to help drive most impact . Evaluate prospective features in AI Services and ability to prioritise them with changing requirements in the direction of AI adoptions. Leadership Outcomes: . Lead with an external perspective, challenge status quo and bring continuous innovation to our existing offerings . Demonstrate learning agility, make decisions quickly and with the highest level of integrity . Lead with a digital mindset and deliver the world's best customer experiences every day Minimum Qualifications Strong Internal Candidate Preferred . 5+ years of building machine learning models using advance AI techniques 4-5 years of experience in working with product/engineering/automation development teams in a fast paced, complex, and dynamic environment. . Experience in leveraging Agile, Scrum or other rapid application development methodologies Undergraduate/Master's/PHD in Computer Science/ Data Science/ Statistics/ Mathematics, MBA from institutes of global repute. Preferred Qualifications Technical Skills: 1. Good coding skills in Python, SPARK, Hive, GIT, API 2. Good Understand of Deep Learning Algorithms 3. Superior math & programming skills and hands-on experience in machine learning tools 4. Understanding of how CI/CD works 5. Exposure to Public Cloud Deployment (AWS, GCP) 6. Proficient in statistical techniques 7. Background in Natural Language Processing - with hands on experience in frameworks like langraph, langchain, vector databases, Agentic frameworks Behavioral Areas: Enterprise Leadership Behaviors . Set The Agenda: Put Enterprise Thinking First, Lead with an External Perspective . Bring Others With You: Build the Best Team, Seek & Provide Coaching Feedback, Make Collaboration Essential . Do It The Right Way: Communicate Frequently, Candidly & Clearly, Make Decisions Quickly & Effectively, Live the Blue Box Values, Great Leadership Demands Courage We back you with benefits that support your holistic well-being so you can be and deliver your best. This means caring for you and your loved ones physical, financial, and mental health, as well as providing the flexibility you need to thrive personally and professionally: Competitive base salaries Bonus incentives Support for financial-well-being and retirement Comprehensive medical, dental, vision, life insurance, and disability benefits (depending on location) Flexible working model with hybrid, onsite or virtual arrangements depending on role and business need Generous paid parental leave policies (depending on your location) Free access to global on-site wellness centers staffed with nurses and doctors (depending on location) Free and confidential counseling support through our Healthy Minds program Career development and training opportunities American Express is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other status protected by law. Offer of employment with American Express is conditioned upon the successful completion of a background verification check, subject to applicable laws and regulations.

AI Solutions Architect with total of around 10-15 years of experience and atleast 5-8 years of relevant data science, analytics and AI/ML. • Develop strategies/solutions to solve problems in logical yet creative ways, leveraging state-of-the-art machine learning, deep learning and GEN AI techniques. • Technically lead a team of data scientists to produce project deliverables on time and with high quality. • Identify and address client needs in different domains, by analyzing large and complex data sets, processing, cleansing, and verifying the integrity of data, and performing exploratory data analysis (EDA) using state-of-the-art methods. • Select features, build and optimize classifiers/regressors, etc. using machine learning and deep learning techniques. • Enhance data collection procedures to include information that is relevant for building analytical systems, and ensure data quality and accuracy. • Perform ad-hoc analysis and present results in a clear manner to both technical and non-technical stakeholders. • Create custom reports and presentations with strong data visualization and storytelling skills to effectively communicate analytical conclusions to senior officials in a company and other stakeholders. • Expertise in data mining, EDA, feature selection, model building, and optimization using machine learning and deep learning techniques. • Strong programming skills in Python. • Excellent communication and interpersonal skills, with the ability to present complex analytical concepts to both technical and non-technical stakeholders. Primary Skills : - Excellent understanding and hand-on experience of data-science and machine learning techniques & algorithms for supervised & unsupervised problems, NLP and computer vision and GEN AI. Good applied statistics skills, such as distributions, statistical inference & testing, etc. - Excellent understanding and hand-on experience on building Deep-learning models for text & image analytics (such as ANNs, CNNs, LSTM, Transfer Learning, Encoder and decoder, etc). - Proficient in coding in common data science language & tools such as R, Python. - Experience with common data science toolkits, such as NumPy, Pandas, Matplotlib, StatsModel, Scikitlearn, SciPy, NLTK, Spacy, OpenCV etc. - Experience with common data science frameworks such as Tensorflow, Keras, PyTorch, XGBoost,etc. - Exposure or knowledge in cloud (Azure/AWS). - Experience on deployment of model in production. Standard Skills: In-depth understanding of manufacturing workflows, production planning, and quality control. Familiarity with ISA-95 and ISA-88 standards for manufacturing systems. Experience working with shop floor automation and IoT devices. Good To have skills: MES Certifications, AI/ML, regulatory experience, and emerging technologies like IoT or edge computing.

• Develop strategies/solutions to solve problems in logical yet creative ways, leveraging state-of-the-art machine learning, deep learning and GEN AI techniques. • Technically lead a team of data scientists to produce project deliverables on time and with high quality. • Identify and address client needs in different domains, by analyzing large and complex data sets, processing, cleansing, and verifying the integrity of data, and performing exploratory data analysis (EDA) using state-of-the-art methods. • Select features, build and optimize classifiers/regressors, etc. using machine learning and deep learning techniques. • Enhance data collection procedures to include information that is relevant for building analytical systems, and ensure data quality and accuracy. • Perform ad-hoc analysis and present results in a clear manner to both technical and non-technical stakeholders. • Create custom reports and presentations with strong data visualization and storytelling skills to effectively communicate analytical conclusions to senior officials in a company and other stakeholders. • Expertise in data mining, EDA, feature selection, model building, and optimization using machine learning and deep learning techniques. • Strong programming skills in Python. • Excellent communication and interpersonal skills, with the ability to present complex analytical concepts to both technical and non-technical stakeholders. Primary Skills : - Excellent understanding and hand-on experience of data-science and machine learning techniques & algorithms for supervised & unsupervised problems, NLP and computer vision and GEN AI. Good applied statistics skills, such as distributions, statistical inference & testing, etc. - Excellent understanding and hand-on experience on building Deep-learning models for text & image analytics (such as ANNs, CNNs, LSTM, Transfer Learning, Encoder and decoder, etc). - Proficient in coding in common data science language & tools such as R, Python. - Experience with common data science toolkits, such as NumPy, Pandas, Matplotlib, StatsModel, Scikitlearn, SciPy, NLTK, Spacy, OpenCV etc. - Experience with common data science frameworks such as Tensorflow, Keras, PyTorch, XGBoost,etc. - Exposure or knowledge in cloud (Azure/AWS). - Experience on deployment of model in production.

Role: Data Scientist – Generative & Agentic AI (Healthcare Domain) Educational Qualification: ME / BE / MCA Experience Required: 4+ Years Shifts: Day Shift Skills & Responsibilities ➤ Experience: 4+ years of experience in Machine Learning, Deep Learning, or Generative AI, with a strong focus on healthcare software product development and medical coding automation. ➤ Programming & Frameworks:  Proficient in Python, with hands-on experience using Pandas, NumPy, and OOPs concepts.  Practical experience with PyTorch, TensorFlow, Keras, and Hugging Face Transformers.  Familiar with writing optimized SQL queries for large-scale structured clinical data. ➤ Healthcare-Specific AI:  Strong understanding of medical coding standards (ICD, CPT, SNOMED), EHR systems, and clinical document processing.  Exposure to HL7, FHIR APIs, and privacy regulations like HIPAA is an added advantage. ➤ Generative AI & NLP:  Experience working with LLMs, GANs, VAEs, and Diffusion Models in healthcare use cases (e.g., clinical summarization, automated coding, documentation assistance).  Familiar with Azure OpenAI, AWS Bedrock, DALL·E, and Stable Diffusion platforms.  Strong grasp of NLP techniques such as Named Entity Recognition (NER), token classification, contextual embeddings, and deep learning models like RNN, LSTM, GRU. ➤ Agentic AI & Autonomous Workflows:  Experience or familiarity with building agentic systems using LangChain, AutoGen, or CrewAI for orchestrating multi-step tasks (e.g., claim validation, document parsing).  Ability to integrate autonomous agents with tool-based systems and APIs to enhance workflow efficiency. ➤ Machine Learning & Statistical Modeling:  Expertise in supervised and unsupervised ML, including Random Forest, SVM, Boosting, Bagging, Regression, and Clustering methods.  Strong capability in feature engineering, model training, and cross-validation for healthcare data. ➤ MLOps, Deployment & Data Integration:  Experience with cloud platforms such as AWS, Azure, or GCP for scalable ML model deployment.  Familiarity with MLOps practices, CI/CD pipelines, Docker, Kubernetes, and model versioning.  Hands-on experience with Apache NiFi for data ingestion, integration, and workflow automation, including designing NiFi flows for structured/unstructured clinical data and seamless integration with downstream ML models.  Proficient with Linux systems and GPU-based ML workflows. ➤ Research, Compliance & Ethics:  Experience contributing to AI research, open-source projects, or Kaggle competitions focused on healthcare or NLP.  Awareness of AI ethics, bias mitigation, explainability techniques, and safe deployment of AI in clinical settings. ➤ Soft Skills & Collaboration:  Proven ability to work independently and in agile teams with product managers, clinical SMEs, and backend engineers.  Effective communication for presenting results, writing technical documentation, and supporting regulatory submissions.  Knowledge of computer vision is a plus for multimodal applications (e.g., diagnostics, image-text synthesis). Interested candidates can share there cv shivangi.manandhaR@etenico.com

The responsibilities for this role include conducting original research on generative AI models, focusing on model architectures, training methods, fine-tuning, and evaluation strategies. You will be responsible for building Proof of Concepts (POCs) with emerging AI innovations and assessing their feasibility for production. Additionally, you will design and experiment with multimodal generative models encompassing text, images, audio, and other modalities. Developing autonomous, agent-based AI systems capable of adaptive decision-making is a key aspect of the role. You will lead the design, training, fine-tuning, and deployment of generative AI systems on large datasets. Optimizing AI algorithms for efficiency, scalability, and computational performance using parallelization, distributed systems, and hardware acceleration will also be part of your responsibilities. Managing data preprocessing and feature engineering, including cleaning, normalization, dimensionality reduction, and feature selection, is essential. You will evaluate and validate models using industry-standard benchmarks, iterating to achieve target KPIs. Providing technical leadership and mentorship to junior researchers and engineers is crucial. Documenting research findings, model architectures, and experimental outcomes in technical reports and publications is also required. It is important to stay updated with the latest advancements in NLP, DL, and generative AI to foster a culture of innovation within the team. The mandatory technical and functional skills for this role include strong expertise in PyTorch or TensorFlow. Proficiency in deep learning architectures such as CNN, RNN, LSTM, Transformers, and LLMs (BERT, GPT, etc.) is necessary. Experience in fine-tuning open-source LLMs (Hugging Face, LLaMA 3.1, BLOOM, Mistral AI, etc.) is required. Hands-on knowledge of PEFT techniques (LoRA, QLoRA, etc.) is expected. Familiarity with emerging AI frameworks and protocols (MCP, A2A, ACP, etc.) is a plus. Deployment experience with cloud AI platforms like GCP Vertex AI, Azure AI Foundry, or AWS SageMaker is essential. A proven track record in building POCs for cutting-edge AI use cases is also important. In terms of the desired candidate profile, experience with LangGraph, CrewAI, or Autogen for agent-based AI is preferred. Large-scale deployment of GenAI/ML projects with MLOps/LLMOps best practices is desirable. Experience in handling scalable data pipelines (BigQuery, Synapse, etc.) is a plus. A strong understanding of cloud computing architectures (Azure, AWS, GCP) is beneficial. Key behavioral attributes for this role include a strong ownership mindset, being able to lead end-to-end project deliverables, not just tasks. The ability to align AI solutions with business objectives and data requirements is crucial. Excellent communication and collaboration skills for cross-functional projects are also important.,

JOB DESCRIPTION Roles & responsibilities Here are some of the key responsibilities of Sr AI Research Scientist: Research and Development: Conduct original research on generative AI models, focusing on model architecture, training methodologies, fine-tuning techniques, and evaluation strategies. Maintain a strong publication record in top-tier conferences and journals, showcasing contributions to the fields of Natural Language Processing (NLP), Deep Learning (DL), and Machine Learning (ML). Experience with POCs on emerging and latest innovation in AI. Multimodal Development: Design and experiment with multimodal generative models that integrate various data types, including text, images, and other modalities to enhance AI capabilities. Agentic AI Systems: Develop and design autonomous AI systems that exhibit agentic behavior, capable of making independent decisions and adapting to dynamic environments. Design and Implementation: Lead the design, development, and implementation of generative AI models and systems, ensuring a deep understanding of the domain. Select suitable models, train them on large datasets, fine-tune hyperparameters, and optimize overall performance. Algorithm Optimization: Optimize generative AI algorithms to enhance their efficiency, scalability, and computational performance through techniques such as parallelization, distributed computing, and hardware acceleration, maximizing the capabilities of modern computing architectures. Data Preprocessing and Feature Engineering: Manage large datasets by performing data preprocessing and feature engineering to extract critical information for generative AI models. This includes tasks such as data cleaning, normalization, dimensionality reduction, and feature selection. Model Evaluation and Validation: Evaluate the performance of generative AI models using relevant metrics and validation techniques. Conduct experiments, analyze results, and iteratively refine models to meet desired performance benchmarks. Technical Leadership: Provide technical leadership and mentorship to junior team members, guiding their development in generative AI through work reviews, skill-building, and knowledge sharing. Ability to drive multiple teams and cross-collaborate to ensure the quality delivery. Documentation and Reporting: Document research findings, model architectures, methodologies, and experimental results thoroughly. Prepare technical reports, presentations, and whitepapers to effectively communicate insights and findings to stakeholders. Continuous Learning and Innovation: Stay abreast of the latest advancements in generative AI by reading research papers, attending conferences, and engaging with relevant communities. Foster a culture of learning and innovation within the team to drive continuous improvement. Mandatory technical & functional skills Machine learning frameworks - PyTorch or TensorFlow. Deep Learning algorithms - CNN, RNN, LSTM, Transformers LLMs ( BERT, GPT, etc.) and NLP algorithms. Design experience for fine Tuning of Open source LLMs from Huggingface, Meta- LLaMA 3.1, BLOOM, Mistral AI etc. GCP : Vertex AI or Azure : AI Foundry or AWS SageMaker Scientific understanding - PEFT - LORA, QLORA, etc. Exposure to GCP : Vertex AI or Azure : AI Foundry or AWS SageMaker In-depth conceptual understanding on emerging and latest innovation in AI. Stay current with AI trends - MCP, A2A protocol, ACP, etc. Preferred Technical & Functional Skills —Langgraph/CrewAI/Autogen —Large scale deployment of GenAI/DL/ML projects, with good understanding of MLOps /LLM Ops —Ensure scalability and efficiency, handle data tasks, —Cloud computing experience- Azure/AWS/GCP —BigQuery/Synapse Key behavioral attributes/requirements —Ability to mentor Managers and Tech Leads —Ability to own project deliverables, not just individual tasks —Understand business objectives and functions to support data needs RESPONSIBILITIES Roles & responsibilities Here are some of the key responsibilities of Sr AI Research Scientist: Research and Development: Conduct original research on generative AI models, focusing on model architecture, training methodologies, fine-tuning techniques, and evaluation strategies. Maintain a strong publication record in top-tier conferences and journals, showcasing contributions to the fields of Natural Language Processing (NLP), Deep Learning (DL), and Machine Learning (ML). Experience with POCs on emerging and latest innovation in AI. Multimodal Development: Design and experiment with multimodal generative models that integrate various data types, including text, images, and other modalities to enhance AI capabilities. Agentic AI Systems: Develop and design autonomous AI systems that exhibit agentic behavior, capable of making independent decisions and adapting to dynamic environments. Design and Implementation: Lead the design, development, and implementation of generative AI models and systems, ensuring a deep understanding of the domain. Select suitable models, train them on large datasets, fine-tune hyperparameters, and optimize overall performance. Algorithm Optimization: Optimize generative AI algorithms to enhance their efficiency, scalability, and computational performance through techniques such as parallelization, distributed computing, and hardware acceleration, maximizing the capabilities of modern computing architectures. Data Preprocessing and Feature Engineering: Manage large datasets by performing data preprocessing and feature engineering to extract critical information for generative AI models. This includes tasks such as data cleaning, normalization, dimensionality reduction, and feature selection. Model Evaluation and Validation: Evaluate the performance of generative AI models using relevant metrics and validation techniques. Conduct experiments, analyze results, and iteratively refine models to meet desired performance benchmarks. Technical Leadership: Provide technical leadership and mentorship to junior team members, guiding their development in generative AI through work reviews, skill-building, and knowledge sharing. Ability to drive multiple teams and cross-collaborate to ensure the quality delivery. Documentation and Reporting: Document research findings, model architectures, methodologies, and experimental results thoroughly. Prepare technical reports, presentations, and whitepapers to effectively communicate insights and findings to stakeholders. Continuous Learning and Innovation: Stay abreast of the latest advancements in generative AI by reading research papers, attending conferences, and engaging with relevant communities. Foster a culture of learning and innovation within the team to drive continuous improvement. Mandatory technical & functional skills Machine learning frameworks - PyTorch or TensorFlow. Deep Learning algorithms - CNN, RNN, LSTM, Transformers LLMs ( BERT, GPT, etc.) and NLP algorithms. Design experience for fine Tuning of Open source LLMs from Huggingface, Meta- LLaMA 3.1, BLOOM, Mistral AI etc. GCP : Vertex AI or Azure : AI Foundry or AWS SageMaker Scientific understanding - PEFT - LORA, QLORA, etc. Exposure to GCP : Vertex AI or Azure : AI Foundry or AWS SageMaker In-depth conceptual understanding on emerging and latest innovation in AI. Stay current with AI trends - MCP, A2A protocol, ACP, etc. Preferred Technical & Functional Skills — Langgraph/CrewAI/Autogen — Large scale deployment of GenAI/DL/ML projects, with good understanding of MLOps /LLM Ops — Ensure scalability and efficiency, handle data tasks, — Cloud computing experience- Azure/AWS/GCP — BigQuery/Synapse Key behavioral attributes/requirements — Ability to mentor Managers and Tech Leads — Ability to own project deliverables, not just individual tasks — Understand business objectives and functions to support data needs QUALIFICATIONS This role is for you if you have the below Educational Qualifications Masters (MS by Research)/PhD or equivalent degree in Computer Science Preferences to research scholars from Tier 1 colleges- IITs, NITs, IISc, IIITs, ISIs, etc. Work Experience 12+ Years of experience with strong record of publications (at least 5) in top tier conferences and journals #KGS

Job Description Roles & responsibilities Here are some of the key responsibilities of AI Tech Lead: Work on the Implementation and Solution delivery of the AI applications leading the team across onshore/offshore and should be able to cross-collaborate across all the AI streams. Design end-to-end AI applications, ensuring integration across multiple commercial and open source tools. Work closely with business analysts and domain experts to translate business objectives into technical requirements and AI-driven solutions and applications. Partner with product management to design agile project roadmaps, aligning technical strategy. Work along with data engineering teams to ensure smooth data flows, quality, and governance across data sources. Lead the design and implementations of reference architectures, roadmaps, and best practices for AI applications. Fast adaptability with the emerging technologies and methodologies, recommending proven innovations. Identify and define system components such as data ingestion pipelines, model training environments, continuous integration/continuous deployment (CI/CD) frameworks, and monitoring systems. Utilize containerization (Docker, Kubernetes) and cloud services to streamline the deployment and scaling of AI systems. Implement robust versioning, rollback, and monitoring mechanisms that ensure system stability, reliability, and performance. Ensure the implementation supports scalability, reliability, maintainability, and security best practices. Project Management: You will oversee the planning, execution, and delivery of AI and ML applications, ensuring that they are completed within budget and timeline constraints. This includes project management defining project goals, allocating resources, and managing risks. Oversee the lifecycle of AI application development—from design to development, testing, deployment, and optimization. Enforce security best practices during each phase of development, with a focus on data privacy, user security, and risk mitigation. Provide mentorship to engineering teams and foster a culture of continuous learning. Lead technical knowledge-sharing sessions and workshops to keep teams up-to-date on the latest advances in generative AI and architectural best practices. Mandatory technical & functional skills Manage multiple projects and teams in parallel with strong cross-collaborative skills. Must have excellent troubleshooting skills. The ideal candidate should have a strong background in working or developing agents using langgraph, autogen, and CrewAI. Proficiency in Python, with robust knowledge of machine learning libraries and frameworks such as TensorFlow, PyTorch, and Keras. Scientific understanding of Deep learning and NLP algorithms – RNN, CNN, LSTM, transformers architecture etc. Familiarity with open source model libraries such as Hugging Face Transformers, OpenAI’s API integrations, and other domain-specific tools. Strong understanding of generative techniques, including GANs, VAEs, diffusion models, and autoregressive models. Training and fine tuning of Large Language Models or SLMs (PALM2, GPT4, LLAMAetc ) Proven experience with cloud computing platforms (AWS, Azure, Google Cloud Platform) for building and deploying scalable AI solutions. Large scale deployment of ML projects, with good understanding of DevOps /MLOps /LLM Ops Hands-on skills with containerization (Docker) and orchestration frameworks (Kubernetes), including related DevOps tools like Jenkins and GitLab CI/CD. Expertise in designing distributed systems, RESTful APIs, GraphQL integrations, and microservices architecture. - Knowledge of event-driven architectures and message brokers (e.g., RabbitMQ, Apache Kafka) to support robust inter-system communications. Open-source contributions or published research in relevant domains. Key behavioral attributes/requirements Ability to mentor junior developers Ability to own project deliverables and contribute towards risk mitigation Understand business objectives and functions to support data needs Key leadership competencies Should drive engaged workforce and uphold positive relationships with employees, to foster a culture of collaboration, innovation, and inclusivity. Quality: Must have the ability to oversee all aspects of quality control processes and ensure that the services provided to the clients meet the highest possible standards by being detail-oriented, analytical, and highly organized. Continuous Improvement: Should be able to identify the areas of improvement and focus on continual improvement Financial Knowledge: Must possess strong financial skills to ensure that the financials of their respective unit are managed to or better than budget. Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) to ensure system reliability and operational performance. Other information Interview process: Technical Interviews and HR Interview Does the job role involve travelling: : Yes (Frequency will be based on Business Requirements) Does the busy season apply to this role?: Yes (At the time of Quarterly filings and Year end filings) Responsibilities Roles & responsibilities Here are some of the key responsibilities of AI Tech Lead: Work on the Implementation and Solution delivery of the AI applications leading the team across onshore/offshore and should be able to cross-collaborate across all the AI streams. Design end-to-end AI applications, ensuring integration across multiple commercial and open source tools. Work closely with business analysts and domain experts to translate business objectives into technical requirements and AI-driven solutions and applications. Partner with product management to design agile project roadmaps, aligning technical strategy. Work along with data engineering teams to ensure smooth data flows, quality, and governance across data sources. Lead the design and implementations of reference architectures, roadmaps, and best practices for AI applications. Fast adaptability with the emerging technologies and methodologies, recommending proven innovations. Identify and define system components such as data ingestion pipelines, model training environments, continuous integration/continuous deployment (CI/CD) frameworks, and monitoring systems. Utilize containerization (Docker, Kubernetes) and cloud services to streamline the deployment and scaling of AI systems. Implement robust versioning, rollback, and monitoring mechanisms that ensure system stability, reliability, and performance. Ensure the implementation supports scalability, reliability, maintainability, and security best practices. Project Management: You will oversee the planning, execution, and delivery of AI and ML applications, ensuring that they are completed within budget and timeline constraints. This includes project management defining project goals, allocating resources, and managing risks. Oversee the lifecycle of AI application development—from design to development, testing, deployment, and optimization. Enforce security best practices during each phase of development, with a focus on data privacy, user security, and risk mitigation. Provide mentorship to engineering teams and foster a culture of continuous learning. Lead technical knowledge-sharing sessions and workshops to keep teams up-to-date on the latest advances in generative AI and architectural best practices. Mandatory technical & functional skills Manage multiple projects and teams in parallel with strong cross-collaborative skills. Must have excellent troubleshooting skills. The ideal candidate should have a strong background in working or developing agents using langgraph, autogen, and CrewAI. Proficiency in Python, with robust knowledge of machine learning libraries and frameworks such as TensorFlow, PyTorch, and Keras. Scientific understanding of Deep learning and NLP algorithms – RNN, CNN, LSTM, transformers architecture etc. Familiarity with open source model libraries such as Hugging Face Transformers, OpenAI’s API integrations, and other domain-specific tools. Strong understanding of generative techniques, including GANs, VAEs, diffusion models, and autoregressive models. Training and fine tuning of Large Language Models or SLMs (PALM2, GPT4, LLAMAetc ) Proven experience with cloud computing platforms (AWS, Azure, Google Cloud Platform) for building and deploying scalable AI solutions. Large scale deployment of ML projects, with good understanding of DevOps /MLOps /LLM Ops Hands-on skills with containerization (Docker) and orchestration frameworks (Kubernetes), including related DevOps tools like Jenkins and GitLab CI/CD. Expertise in designing distributed systems, RESTful APIs, GraphQL integrations, and microservices architecture. - Knowledge of event-driven architectures and message brokers (e.g., RabbitMQ, Apache Kafka) to support robust inter-system communications. Open-source contributions or published research in relevant domains. Key behavioral attributes/requirements Ability to mentor junior developers Ability to own project deliverables and contribute towards risk mitigation Understand business objectives and functions to support data needs Key leadership competencies Should drive engaged workforce and uphold positive relationships with employees, to foster a culture of collaboration, innovation, and inclusivity. Quality: Must have the ability to oversee all aspects of quality control processes and ensure that the services provided to the clients meet the highest possible standards by being detail-oriented, analytical, and highly organized. Continuous Improvement: Should be able to identify the areas of improvement and focus on continual improvement Financial Knowledge: Must possess strong financial skills to ensure that the financials of their respective unit are managed to or better than budget. Experience with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) to ensure system reliability and operational performance. Other information Interview process: Technical Interviews and HR Interview Does the job role involve travelling: : Yes (Frequency will be based on Business Requirements) Does the busy season apply to this role?: Yes (At the time of Quarterly filings and Year end filings) Qualifications This role is for you if you have the below Educational Qualifications Bachelor’s/Master’s degree in Computer Science Certifications in Cloud technologies (AWS, Azure, GCP) and must have TOGAF certification or any equivalent certification Work experience: 14+ Years of Experience #KGS

At American Express, our culture is built on a 175-year history of innovation, shared values and Leadership Behaviors, and an unwavering commitment to back our customers, communities, and colleagues. As part of Team Amex, you'll experience this powerful backing with comprehensive support for your holistic well-being and many opportunities to learn new skills, develop as a leader, and grow your career. Here, your voice and ideas matter, your work makes an impact, and together, you will help us define the future of American Express. How will you make an impact in this role? Function Description: With a focus on digitization, innovation, and analytics, the Global Decision Sciences (GDS) team creates central, scalable platforms and customer experiences to help markets across all of these priorities. Charter is to drive scale for the business, and accelerate innovation for both immediate impact as well as long-term transformation of our business. A unique aspect of GDS is the integration of diverse skills across all of its remit. GDS has a very broad range of responsibilities, resulting in a broad range of initiatives around the world. The Amex AI Labs of American Express Corporation is looking for best in class Senior Research Engineers to work in a dynamic and rewarding workplace. Amex AI Labs is currently co-located in Bangalore, Gurgaon and New York and involves dynamic and talented researchers with Ph.D. or Master’s degree from top tier institutes in India and abroad having research and product development expertise in AI/ML technologies such as - Deep Learning, Data Science, Natural Language Processing, Cloud Technologies and related technical areas. Going beyond building products and solutions for traditional financial domains such as credit and fraud, the team is also working on challenging research problems in domains such as services, human resource management, travel & lifestyle, and many more. The key goal is to progress the state of the art in science and develop products to solve business problems, in a domain-agnostic manner, for American Express. Purpose of the Role: Collaborate on next generation research services in the areas of Artificial Intelligence (AI) to aid solutions to complex business problems for American Express Responsibilities: Amex AI Labs is looking for individuals with AI R&D background for AI Research & Services team in Bangalore/Gurgaon. The team works on handling capabilities centred on Document Analysis, NLP, AI research from conception to launch. Successful candidates are expected to partner with different business and technology teams in American Express to build capabilities for high impact use-cases. We are looking for extremely agile and entrepreneurial candidates who will help innovate and execute product initiatives across the company. · Lead the ideation and launch of innovative AI/ML capabilities & services · Hands on Deep learning Algorithms like BERT, RNN, LSTM, Transformer, GRU, GAN etc · Understanding Generative AI based closed source models like ChatGPT, Claude, Llama for different usecases · Fine Tuning open source models like Llama and other models on domain specific data · Working with MLOPs capability like MLFlow, AirFlow etc and understanding of deployment capabilities on public cloud (AWS, GCP) and on-prem infrastructure · Technically hands-on working with big data sets using Python/JAVA/SPARK, doing EDA and bringing insights from data · Building POCs basis on research papers analysis while simulating business impact and Transform MVPs to production grade capabilities in collaboration with engineering teams · Authoring and publishing papers, adopting open source tools, presenting in conferences, conducting R&D driving company revenue. · Innovate ways to evangelize the product to drive Amex wide user adoption · Contribute to the strategy and roadmap of AI/ML Initiatives to help drive most impact · Evaluate prospective features in AI Services and ability to prioritise them with changing requirements in the direction of AI adoptions. Leadership Outcomes: · Lead with an external perspective, challenge status quo and bring continuous innovation to our existing offerings · Demonstrate learning agility, make decisions quickly and with the highest level of integrity · Lead with a digital mindset and deliver the world’s best customer experiences every day Minimum Qualifications Strong Internal Candidate Preferred · 5+ years of building machine learning models using advance AI techniques 4-5 years of experience in working with product/engineering/automation development teams in a fast paced, complex, and dynamic environment. · Experience in leveraging Agile, Scrum or other rapid application development methodologies Undergraduate/Master’s/PHD in Computer Science/ Data Science/ Statistics/ Mathematics, MBA from institutes of global repute. Preferred Qualifications Technical Skills: 1. Good coding skills in Python, SPARK, Hive, GIT, API 2. Good Understand of Deep Learning Algorithms 3. Superior math & programming skills and hands-on experience in machine learning tools 4. Understanding of how CI/CD works 5. Exposure to Public Cloud Deployment (AWS, GCP) 6. Proficient in statistical techniques 7. Background in Natural Language Processing - with hands on experience in frameworks like langraph, langchain, vector databases, Agentic frameworks Behavioral Areas: Enterprise Leadership Behaviors · Set The Agenda: Put Enterprise Thinking First, Lead with an External Perspective · Bring Others With You: Build the Best Team, Seek & Provide Coaching Feedback, Make Collaboration Essential · Do It The Right Way: Communicate Frequently, Candidly & Clearly, Make Decisions Quickly & Effectively, Live the Blue Box Values, Great Leadership Demands Courage We back you with benefits that support your holistic well-being so you can be and deliver your best. This means caring for you and your loved ones' physical, financial, and mental health, as well as providing the flexibility you need to thrive personally and professionally: Competitive base salaries Bonus incentives Support for financial-well-being and retirement Comprehensive medical, dental, vision, life insurance, and disability benefits (depending on location) Flexible working model with hybrid, onsite or virtual arrangements depending on role and business need Generous paid parental leave policies (depending on your location) Free access to global on-site wellness centers staffed with nurses and doctors (depending on location) Free and confidential counseling support through our Healthy Minds program Career development and training opportunities American Express is an equal opportunity employer and makes employment decisions without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, disability status, age, or any other status protected by law. Offer of employment with American Express is conditioned upon the successful completion of a background verification check, subject to applicable laws and regulations.

Job Posting TitleSR. DATA SCIENTIST Band/Level5-2-C Education ExperienceBachelors Degree (High School +4 years) Employment Experience5-7 years At TE, you will unleash your potential working with people from diverse backgrounds and industries to create a safer, sustainable and more connected world. Job Overview Solves complex problems and help stakeholders make data- driven decisions by leveraging quantitative methods, such as machine learning. It often involves synthesizing large volume of information and extracting signals from data in a programmatic way. Roles & Responsibilities Key Responsibilities Design, train, and evaluate supervised & unsupervised models (regression, classification, clustering, uplift). Apply automated hyperparameter optimization (Optuna, HyperOpt) and interpretability techniques (SHAP, LIME). Perform deep exploratory data analysis (EDA) to uncover patterns & anomalies. Engineer predictive features from structured, semistructured, and unstructured data; manage feature stores (Feast). Ensure data quality through rigorous validation and automated checks. Build hierarchical, intermittent, and multiseasonal forecasts for thousands of SKUs. Implement traditional (ARIMA, ETS, Prophet) and deeplearning (RNN/LSTM, TemporalFusion Transformer) approaches. Reconcile forecasts across product/category hierarchies; quantify accuracy (MAPE, WAPE) and bias. Establish model tracking & registry (MLflow, SageMaker Model Registry). Develop CI/CD pipelines for automated retraining, validation, and deployment (Airflow, Kubeflow, GitHub Actions). Monitor data & concept drift; trigger retuning or rollback as needed. Design and analyze A/B tests, causal inference studies, and Bayesian experiments. Provide statisticallygrounded insights and recommendations to stakeholders. Translate business objectives into datadriven solutions; present findings to exec & nontech audiences. Mentor junior data scientists, review code/notebooks, and champion best practices. Desired Candidate Minimum Qualifications M.S. in Statistics (preferred) or related field such as Applied Mathematics, Computer Science, Data Science. 5+ years building and deploying ML models in production. Expertlevel proficiency in Python (Pandas, NumPy, SciPy, scikitlearn), SQL, and Git. Demonstrated success delivering largescale demandforecasting or timeseries solutions. Handson experience with MLOps tools (MLflow, Kubeflow, SageMaker, Airflow) for model tracking and automated retraining. Solid grounding in statistical inference, hypothesis testing, and experimental design. Preferred / NicetoHave Experience in supplychain, retail, or manufacturing domains with highgranularity SKU data. Familiarity with distributed data frameworks (Spark, Dask) and cloud data warehouses (BigQuery, Snowflake). Knowledge of deeplearning libraries (PyTorch, TensorFlow) and probabilistic programming (PyMC, Stan). Strong datavisualization skills (Plotly, Dash, Tableau) for storytelling and insight communication. Competencies ABOUT TE CONNECTIVITY TE Connectivity plc (NYSETEL) is a global industrial technology leader creating a safer, sustainable, productive, and connected future. Our broad range of connectivity and sensor solutions enable the distribution of power, signal and data to advance next-generation transportation, energy networks, automated factories, data centers, medical technology and more. With more than 85,000 employees, including 9,000 engineers, working alongside customers in approximately 130 countries, TE ensures that EVERY CONNECTION COUNTS. Learn more atwww.te.com and onLinkedIn , Facebook , WeChat, Instagram and X (formerly Twitter). WHAT TE CONNECTIVITY OFFERS: We are pleased to offer you an exciting total package that can also be flexibly adapted to changing life situations - the well-being of our employees is our top priority! Competitive Salary Package Performance-Based Bonus Plans Health and Wellness Incentives Employee Stock Purchase Program Community Outreach Programs / Charity Events IMPORTANT NOTICE REGARDING RECRUITMENT FRAUD TE Connectivity has become aware of fraudulent recruitment activities being conducted by individuals or organizations falsely claiming to represent TE Connectivity. Please be advised that TE Connectivity never requests payment or fees from job applicants at any stage of the recruitment process. All legitimate job openings are posted exclusively on our official careers website at te.com/careers, and all email communications from our recruitment team will come only from actual email addresses ending in @te.com . If you receive any suspicious communications, we strongly advise you not to engage or provide any personal information, and to report the incident to your local authorities. Across our global sites and business units, we put together packages of benefits that are either supported by TE itself or provided by external service providers. In principle, the benefits offered can vary from site to site.

We are seeking an accomplished Senior Technical Specialist Data Science to lead cutting-edge AI/ML initiatives, design scalable data-driven solutions, and drive innovation across the enterprise. This role requires deep expertise in AI/ML, cloud technologies (Azure), data engineering, and MLOps , with proven experience in leading teams and delivering AI-powered solutions to solve complex business challenges. Key Responsibilities Leadership & Mentorship : Lead, mentor, and inspire a team of Data Scientists to deliver enterprise-grade AI/ML solutions. Solution Architecture : Design and implement end-to-end AI/ML architectures for complex business problems. Generative AI & NLP : Develop Generative AI applications, including RAG-based chatbots, leveraging LLMs, LangChain, LlamaIndex, and Azure OpenAI. MLOps & Deployment : Build, deploy, and monitor scalable ML pipelines using MLFlow, Kubeflow, ClearML, and Azure MLOps frameworks. Advanced Modeling : Deliver predictive modeling, deep learning, and NLP solutions using TensorFlow, PyTorch, BERT, and advanced ML algorithms. Data Visualization & Insights : Perform data analysis and develop interactive dashboards using Tableau, Power BI, Streamlit, and Matplotlib. Cloud & DevOps Integration : Collaborate with DevOps teams to automate ML workflows, optimize infrastructure with Docker/Jenkins, and ensure model scalability and reliability. Strategic Collaboration : Partner with business leaders to define AI strategy, drive data-driven decision-making, and deliver measurable business impact. Innovation & Research : Stay at the forefront of AI/ML advancements, applying emerging techniques to improve accuracy, efficiency, and scalability. Required Skills & Expertise Programming & Scripting : Python, PowerShell, Bash, Perl Machine Learning & Deep Learning : SVM, KNN, XGBoost, LSTM, TensorFlow, PyTorch NLP & Generative AI : Spacy, BERT, LangChain, LlamaIndex, LLOps frameworks Data Visualization : Tableau, Power BI, Streamlit, Matplotlib Cloud & MLOps : Azure ML Studio, Azure OpenAI, Docker, Jenkins, GitHub, MLFlow, ClearML, Kubeflow Databases & Big Data : MS-SQL, Data Preprocessing, Feature Engineering Qualifications Bachelors or Master’s in Computer Science, Data Science, AI/ML, or related field. 10+ years of experience in data science/AI, with at least 3+ years in a leadership role . Proven track record of delivering AI/ML solutions at scale in enterprise environments.

Job Description Roles & responsibilities Here are some of the key responsibilities of AI Research Scientist: Research and Development: Conduct original research on generative AI models, focusing on model architecture, training methodologies, fine-tuning techniques, and evaluation strategies. Maintain a strong publication record in top-tier conferences and journals, showcasing contributions to the fields of Natural Language Processing (NLP), Deep Learning (DL), and Machine Learning (ML). Multimodal Development: Design and experiment with multimodal generative models that integrate various data types, including text, images, and other modalities to enhance AI capabilities. Develop POCs and Showcase it to the stakeholders. Agentic AI Systems: Develop and design autonomous AI systems that exhibit agentic behavior, capable of making independent decisions and adapting to dynamic environments. Model Development and Implementation: Lead the design, development, and implementation of generative AI models and systems, ensuring a deep understanding of the problem domain. Select suitable models, train them on large datasets, fine-tune hyperparameters, and optimize overall performance. Algorithm Optimization: Optimize generative AI algorithms to enhance their efficiency, scalability, and computational performance through techniques such as parallelization, distributed computing, and hardware acceleration, maximizing the capabilities of modern computing architectures. Data Preprocessing and Feature Engineering: Manage large datasets by performing data preprocessing and feature engineering to extract critical information for generative AI models. This includes tasks such as data cleaning, normalization, dimensionality reduction, and feature selection. Model Evaluation and Validation: Evaluate the performance of generative AI models using relevant metrics and validation techniques. Conduct experiments, analyze results, and iteratively refine models to meet desired performance benchmarks. Technical Mentorship: Provide technical leadership and mentorship to junior team members, guiding their development in generative AI through work reviews, skill-building, and knowledge sharing. Documentation and Reporting: Document research findings, model architectures, methodologies, and experimental results thoroughly. Prepare technical reports, presentations, and whitepapers to effectively communicate insights and findings to stakeholders. Continuous Learning and Innovation: Stay abreast of the latest advancements in generative AI by reading research papers, attending conferences, and engaging with relevant communities. Foster a culture of learning and innovation within the team to drive continuous improvement. Mandatory technical & functional skills Strong programming skills in Python and frameworks like PyTorch or TensorFlow. Scientific understanding and In depth knowledge on Deep Learning - CNN, RNN, LSTM, Transformers LLMs ( BERT, GEPT, etc.) and NLP algorithms. Also, familiarity with frameworks like Langgraph/CrewAI/Autogen to develop, deploy and evaluate AI agents. Ability to test and deploy open source LLMs from Huggingface, Meta- LLaMA 3.1, BLOOM, Mistral AI etc. Hands-on ML platforms offered through GCP : Vertex AI or Azure : AI Foundry or AWS SageMaker Preferred Technical & Functional Skills —Ability to create detailed technical architecture with scalability in mind for the AI solutions. Ability to explore hyperscalers and provide comparative analysis across different tools. —Cloud computing experience, particularly with Google/AWS/Azure Cloud Platform, is essential. With strong foundation in understating Data Analytics Services offered by Google/AWS/Azure ( BigQuery/Synapse) —Large scale deployment of GenAI/DL/ML projects, with good understanding of MLOps /LLM Ops Key behavioral attributes/requirements —Ability to mentor junior developers —Ability to own project deliverables, not just individual tasks Understand business objectives and functions to support data needs #KGS Responsibilities Roles & responsibilities Here are some of the key responsibilities of AI Research Scientist: Research and Development: Conduct original research on generative AI models, focusing on model architecture, training methodologies, fine-tuning techniques, and evaluation strategies. Maintain a strong publication record in top-tier conferences and journals, showcasing contributions to the fields of Natural Language Processing (NLP), Deep Learning (DL), and Machine Learning (ML). Multimodal Development: Design and experiment with multimodal generative models that integrate various data types, including text, images, and other modalities to enhance AI capabilities. Develop POCs and Showcase it to the stakeholders. Agentic AI Systems: Develop and design autonomous AI systems that exhibit agentic behavior, capable of making independent decisions and adapting to dynamic environments. Model Development and Implementation: Lead the design, development, and implementation of generative AI models and systems, ensuring a deep understanding of the problem domain. Select suitable models, train them on large datasets, fine-tune hyperparameters, and optimize overall performance. Algorithm Optimization: Optimize generative AI algorithms to enhance their efficiency, scalability, and computational performance through techniques such as parallelization, distributed computing, and hardware acceleration, maximizing the capabilities of modern computing architectures. Data Preprocessing and Feature Engineering: Manage large datasets by performing data preprocessing and feature engineering to extract critical information for generative AI models. This includes tasks such as data cleaning, normalization, dimensionality reduction, and feature selection. Model Evaluation and Validation: Evaluate the performance of generative AI models using relevant metrics and validation techniques. Conduct experiments, analyze results, and iteratively refine models to meet desired performance benchmarks. Technical Mentorship: Provide technical leadership and mentorship to junior team members, guiding their development in generative AI through work reviews, skill-building, and knowledge sharing. Documentation and Reporting: Document research findings, model architectures, methodologies, and experimental results thoroughly. Prepare technical reports, presentations, and whitepapers to effectively communicate insights and findings to stakeholders. Continuous Learning and Innovation: Stay abreast of the latest advancements in generative AI by reading research papers, attending conferences, and engaging with relevant communities. Foster a culture of learning and innovation within the team to drive continuous improvement. Mandatory technical & functional skills Strong programming skills in Python and frameworks like PyTorch or TensorFlow. Scientific understanding and In depth knowledge on Deep Learning - CNN, RNN, LSTM, Transformers LLMs ( BERT, GEPT, etc.) and NLP algorithms. Also, familiarity with frameworks like Langgraph/CrewAI/Autogen to develop, deploy and evaluate AI agents. Ability to test and deploy open source LLMs from Huggingface, Meta- LLaMA 3.1, BLOOM, Mistral AI etc. Hands-on ML platforms offered through GCP : Vertex AI or Azure : AI Foundry or AWS SageMaker Qualifications This role is for you if you have the below Educational Qualifications B.Tech/Masters (MS by Research)/PhD or equivalent degree in Computer Science Preferences to candidates from Tier 1 Colleges as IITs, NITs, IIITs, IISc, Indian Statistical Institute, etc. Work Experience 3-5 Years of experience with strong record of publications (at least 4) in top tier conferences and journals

Job description : ExcelR is seeking experienced AI and Machine Learning trainers to deliver a comprehensive AI and Machine Learning curriculum. The training program will span 40 working days, with daily classes of 5–6 hours, tentatively from August 20, 2025, to October 21, 2025. The role involves teaching theoretical concepts, guiding hands-on projects, and mentoring students on real-world AI/ML applications. Skills Required : Foundational ML: Logistic Regression, SVM, Decision Trees, Ensemble Methods (Bagging, Random Forests), Boosting (Gradient Boosting, AdaBoost, XGBoost, LightGBM), PCA, Clustering (K-means, Hierarchical, DBSCAN), Market Basket Analysis, and Recommendation Systems. Deep Learning: ANN, CNN, RNN, LSTM, GRU, Transformers, GANs, Autoencoders, Diffusion Models (Stable Diffusion), and LLMs (e.g., GPT, BERT). Data Preprocessing: Standardization, normalization, encoding, train-test split, cross-validation, regularization (Lasso, Ridge, ElasticNet), and feature engineering. Generative AI: Text-to-image/audio/video generation, chatbots, sentiment analysis, and Retrieval-Augmented Generation (RAG) with FAISS. Web & Cloud Deployment: Building and deploying RESTful APIs using Flask/FastAPI, cloud computing with AWS (EC2, S3, Lambda) and Azure (Blob, App Services), and Streamlit for project deployment. Mathematics: Calculus, vector algebra, probability. Prompt Engineering: Zero-shot, few-shot, prompt tuning, and designing effective prompts. Case Studies: Hands-on projects (e.g., Bangalore housing prices, Breast cancer classification, Sales dataset). Qualifications : Education: Bachelor’s/Master’s in Computer Science, AI, ML, or related fields. Experience: 3–5 years in Machine Learning, Deep Learning, and Full Stack AI development, with hands-on experience in: Python and libraries like scikit-learn, TensorFlow, Keras, PyTorch. Generative AI (GANs, Diffusion Models, LLMs like GPT-2, LLaMA). Web development (Flask, FastAPI) and cloud deployment (AWS EC2, Azure). Tools like Hugging Face, FAISS, LangChain, and Streamlit. Teaching Skills: Prior teaching/training experience preferred, with the ability to explain complex topics (e.g., attention mechanisms, PCA) to undergraduate students. Certifications: Relevant certifications in AI/ML (e.g., AWS Certified Machine Learning, Google Professional ML Engineer) are a plus.

- Need to be able to communicate their findings, orally and visually. - Need to understand how the products are developed and even more important, as big data touches the privacy of consumers, they need to have a set of ethical responsibilities.- - Need to have a natural desire to go beneath the surface of a problem. - Confident and self-secure as they more often than not will have to deal with situations where there is a lot of unknown. - Good knowledge & experience in Artificial Neural Network ( ANN ), AI conversational Chatbot & Needs to be able to write the required Algorithm and coding programming. - Preferably in different programming languages such as Python, Tensor Flow, Keras, AWS (ML and DL), Numpy, and Pytorch. In addition the need to be familiar with disciplines as follows : - Natural Language Processing, Machine learning, Conceptual modeling, Statistical analysis, Predictive Modeling, and Hypothesis testing. Data scientists should have at least some of the following capabilities : - Cleaning data, formatting data, Having the ability to query databases & perform statistical analysis, building AI Data Modelling, validating the AI Model, and deployment. - Being able to develop or program databases. - Having a good understanding of design & architecture principles.

The role at Novartis as part of the commercial Data and Analytics team involves understanding complex and critical business problems to formulate integrated analytical approaches. You will be required to mine data sources, employ statistical methods, machine learning algorithms, and AI to discover actionable insights and automate processes for efficiency. Your responsibilities include excellent communication skills, collaborating with senior stakeholders, translating business requirements into problem statements, and guiding data scientists in project delivery. You must also have advanced technical skills in data science languages like Python, Databricks, and Snowflake, along with experience in building statistical, econometrics, and machine learning algorithms. Moreover, you should be proficient in developing scalable solutions, understanding advanced optimization techniques, and working with multi-cloud environments such as AWS and MS Azure. The ideal candidate for this position should possess a Masters or Ph.D. in Computer Science, Bioinformatics, or related fields, along with a post-graduate degree in Business Management. A minimum of 12 years of experience in Pharma/Healthcare domain/Life sciences and 5 years of international experience is required. Novartis offers a collaborative and inclusive environment where new insights and solutions are encouraged at the intersection of medical science and digital innovation. The company believes in fostering a diverse and equitable workplace that inspires innovative ways of working. By joining Novartis, you will have the opportunity to collaborate with like-minded professionals to tackle the world's toughest medical challenges. If you are passionate about making a difference and are ready to take on new challenges, Novartis welcomes you to be a part of their team. Novartis is committed to providing reasonable accommodation to individuals with disabilities throughout the recruitment process. If you require assistance due to a medical condition or disability, please contact [email protected] with your request and contact information, specifying the job requisition number. Join Novartis in creating a brighter future together by exploring suitable career opportunities within their network. To learn more about the benefits and rewards offered at Novartis, visit their website. The company's dedication to building an inclusive work environment ensures that their teams are representative of the patients and communities they serve.,

Your Team Responsibilities The ESG & Climate Carbon Markets Data Science team builds intelligent, scalable AI applications that power MSCI’s carbon and climate analytics platforms. In this role, you will bring cutting-edge AI/ML techniques to production, including agentic modelling, LLM integration, time series forecasting, and carbon-specific prediction systems. You will collaborate with data scientists, engineers, and product owners to transform ideas into deployable tools. Your Key Responsibilities Design, build, and deploy AI-powered applications and forecasting systems in Python Leverage LLMs, agentic frameworks (e.g., Vertyex AI, LangChain), and generative AI to automate research and analysis Develop time series and ML models (e.g., XGBoost, LSTM) to forecast carbon prices and project outcomes Build interactive user interfaces using Dash, Streamlit, or React Maintain cloud-native workflows in GCP (Vertex AI, Cloud Run/Job) Ensure code quality through unit testing and infrastructure practices (Terraform a plus) Collaborate with cross-functional teams to translate business needs into technical solutions Your Skills And Experience That Will Help You Excel Strong Python skills with production-grade development experience Experience using LLMs and agentic modelling frameworks Knowledge of machine learning pipelines and cloud tools (GCP preferred) Hands-on work with forecasting models and unstructured data processing Strong UI development experience with Dash, Streamlit, or React Familiarity with CI/CD and infrastructure-as-code (Terraform is a plus) Ability to work independently while aligning with product strategy and data governance About MSCI What we offer you Transparent compensation schemes and comprehensive employee benefits, tailored to your location, ensuring your financial security, health, and overall wellbeing. Flexible working arrangements, advanced technology, and collaborative workspaces. A culture of high performance and innovation where we experiment with new ideas and take responsibility for achieving results. A global network of talented colleagues, who inspire, support, and share their expertise to innovate and deliver for our clients. Global Orientation program to kickstart your journey, followed by access to our Learning@MSCI platform, LinkedIn Learning Pro and tailored learning opportunities for ongoing skills development. Multi-directional career paths that offer professional growth and development through new challenges, internal mobility and expanded roles. We actively nurture an environment that builds a sense of inclusion belonging and connection, including eight Employee Resource Groups. All Abilities, Asian Support Network, Black Leadership Network, Climate Action Network, Hola! MSCI, Pride & Allies, Women in Tech, and Women’s Leadership Forum. At MSCI we are passionate about what we do, and we are inspired by our purpose – to power better investment decisions. You’ll be part of an industry-leading network of creative, curious, and entrepreneurial pioneers. This is a space where you can challenge yourself, set new standards and perform beyond expectations for yourself, our clients, and our industry. MSCI is a leading provider of critical decision support tools and services for the global investment community. With over 50 years of expertise in research, data, and technology, we power better investment decisions by enabling clients to understand and analyze key drivers of risk and return and confidently build more effective portfolios. We create industry-leading research-enhanced solutions that clients use to gain insight into and improve transparency across the investment process. MSCI Inc. is an equal opportunity employer. It is the policy of the firm to ensure equal employment opportunity without discrimination or harassment on the basis of race, color, religion, creed, age, sex, gender, gender identity, sexual orientation, national origin, citizenship, disability, marital and civil partnership/union status, pregnancy (including unlawful discrimination on the basis of a legally protected parental leave), veteran status, or any other characteristic protected by law. MSCI is also committed to working with and providing reasonable accommodations to individuals with disabilities. If you are an individual with a disability and would like to request a reasonable accommodation for any part of the application process, please email Disability.Assistance@msci.com and indicate the specifics of the assistance needed. Please note, this e-mail is intended only for individuals who are requesting a reasonable workplace accommodation; it is not intended for other inquiries. To all recruitment agencies MSCI does not accept unsolicited CVs/Resumes. Please do not forward CVs/Resumes to any MSCI employee, location, or website. MSCI is not responsible for any fees related to unsolicited CVs/Resumes. Note on recruitment scams We are aware of recruitment scams where fraudsters impersonating MSCI personnel may try and elicit personal information from job seekers. Read our full note on careers.msci.com

Job description : ExcelR is seeking experienced AI and Machine Learning trainers to deliver a comprehensive AI and Machine Learning curriculum. The training program will span 40 working days, with daily classes of 5–6 hours, tentatively from August 20, 2025, to October 21, 2025. The role involves teaching theoretical concepts, guiding hands-on projects, and mentoring students on real-world AI/ML applications. Skills Required : Foundational ML: Logistic Regression, SVM, Decision Trees, Ensemble Methods (Bagging, Random Forests), Boosting (Gradient Boosting, AdaBoost, XGBoost, LightGBM), PCA, Clustering (K-means, Hierarchical, DBSCAN), Market Basket Analysis, and Recommendation Systems. Deep Learning: ANN, CNN, RNN, LSTM, GRU, Transformers, GANs, Autoencoders, Diffusion Models (Stable Diffusion), and LLMs (e.g., GPT, BERT). Data Preprocessing: Standardization, normalization, encoding, train-test split, cross-validation, regularization (Lasso, Ridge, ElasticNet), and feature engineering. Generative AI: Text-to-image/audio/video generation, chatbots, sentiment analysis, and Retrieval-Augmented Generation (RAG) with FAISS. Web & Cloud Deployment: Building and deploying RESTful APIs using Flask/FastAPI, cloud computing with AWS (EC2, S3, Lambda) and Azure (Blob, App Services), and Streamlit for project deployment. Mathematics: Calculus, vector algebra, probability. Prompt Engineering: Zero-shot, few-shot, prompt tuning, and designing effective prompts. Case Studies: Hands-on projects (e.g., Bangalore housing prices, Breast cancer classification, Sales dataset). Qualifications : Education: Bachelor’s/Master’s in Computer Science, AI, ML, or related fields. Experience: 3–5 years in Machine Learning, Deep Learning, and Full Stack AI development, with hands-on experience in: Python and libraries like scikit-learn, TensorFlow, Keras, PyTorch. Generative AI (GANs, Diffusion Models, LLMs like GPT-2, LLaMA). Web development (Flask, FastAPI) and cloud deployment (AWS EC2, Azure). Tools like Hugging Face, FAISS, LangChain, and Streamlit. Teaching Skills: Prior teaching/training experience preferred, with the ability to explain complex topics (e.g., attention mechanisms, PCA) to undergraduate students. Certifications: Relevant certifications in AI/ML (e.g., AWS Certified Machine Learning, Google Professional ML Engineer) are a plus.

Skills: Python, Machine Learning, Deep Learning, Predictive Modeling, Natural Language Processing (NLP), Data Science, Gen AI, RAG, Greetings from Colan Infotech!! Role - Data Scientist Experience - 6+ Years Job Location - Chennai/Bangalore Notice Period - Immediate to 30 Days Primary Skills Needed : AI/ML, Tensorflow, Django, Pytorch, NLP, Image processing,Gen AI,LLM Secondary Skills Needed : Keras, OpenCV, Azure or AWS Job Description:- Practical knowledge and working experience on Statistics and Operation Research methods. Practical knowledge and working experience in tools and frameworks like Flask, PySpark, Pytorch, tensorflow, keras, Databricks, OpenCV, Pillow/PIL, streamlit, d3js, dashplotly, neo4j. Good understanding of how to apply predictive and machine learning techniques like regression models, XGBoost, random forest, GBM, Neural Nets, SVM etc. Proficient with NLP techniques like RNN, LSTM and Attention based models and effectively handle readily available stanford, Azure, Open AI NLP models. Good understanding of SQL from a perspective of how to write efficient queries for pulling the data from database. Hands on experience on any version control tool (github, bitbucket). Experience of deploying ML models into production environment experience (MLOps) in any one of the cloud platforms like Azure and AWS Comprehend business issues and propose valuable business solutions. Design Factual or AI/profound learning models to address business issues. Design Statistical Models/ML/DL models and deploy them for production. Formulate what information is accessible from where and how to augment it. Develop innovative graphs for data comprehension using d3js, dashplotly and neo4j

Skills: Python, Machine Learning, Deep Learning, Predictive Modeling, Natural Language Processing (NLP), Data Science, Gen AI, RAG, Greetings from Colan Infotech!! Role - Data Scientist Experience - 6+ Years Job Location - Chennai/Bangalore Notice Period - Immediate to 30 Days Primary Skills Needed : AI/ML, Tensorflow, Django, Pytorch, NLP, Image processing,Gen AI,LLM Secondary Skills Needed : Keras, OpenCV, Azure or AWS Job Description:- Practical knowledge and working experience on Statistics and Operation Research methods. Practical knowledge and working experience in tools and frameworks like Flask, PySpark, Pytorch, tensorflow, keras, Databricks, OpenCV, Pillow/PIL, streamlit, d3js, dashplotly, neo4j. Good understanding of how to apply predictive and machine learning techniques like regression models, XGBoost, random forest, GBM, Neural Nets, SVM etc. Proficient with NLP techniques like RNN, LSTM and Attention based models and effectively handle readily available stanford, Azure, Open AI NLP models. Good understanding of SQL from a perspective of how to write efficient queries for pulling the data from database. Hands on experience on any version control tool (github, bitbucket). Experience of deploying ML models into production environment experience (MLOps) in any one of the cloud platforms like Azure and AWS Comprehend business issues and propose valuable business solutions. Design Factual or AI/profound learning models to address business issues. Design Statistical Models/ML/DL models and deploy them for production. Formulate what information is accessible from where and how to augment it. Develop innovative graphs for data comprehension using d3js, dashplotly and neo4j

About Position: We are seeking passionate and innovative Generative AI Technology team to join our organization. As a Architect in Gen AI technology, you will work at the intersection of technology and industry knowledge, leveraging your expertise in generative AI tools to push the boundaries for building innovative solutions. As a Generative AI Technologist, you will play a crucial role in shaping and building the future of building solutions across business functions, industry domains and finding unique ways to bend emerging technologies. We are looking for GenAI Architect to develop high-impact, high-visibility Large language models and & improve the experience of millions of customers. If you're creative & passionate about solving real world conversational AI problems, come join us. Role: AI/ML Architect Location: All Persistent Locations Experience: 8+ Years Job Type: Full Time Employment What You'll Do: Develop and Propose the solution architecture for Generative AI (especially LLMs), advanced Conversational AI chatbots & cloud AIaaS. Develop and implement applications leveraging advanced Generative AI models such as OpenAI GPT, Anthropic Clude, Meta Llama2 and Google Gemini focusing on enhancing developer and business productivity. Must have experience with AWS Bedrock/Azure AI Studio Knowledge on UI technologies like TypeScript, React, Next.JS and Tailwind CSS. Hands-on experience with deployment across cloud environment Develop, Train, Finetune, and Deploy large language models to perform the domain specific tasks. Apply instruction tuning, reinforcement learning from human feedback (RLHF), and parameter efficient finetuning such as, adaptors, LoRA, and so on to improve LLMs for different use cases. Architect solutions incorporating advanced techniques like Retrieval Augmented Generation (RAG), Transformer Architectures, Lanchain, Sqlchain ensuring optimal model performance and scalability. Hands on experience in RAG system, LLM fine tuning and Vector DB is must Measure and benchmark of the various LLMs and application performance. Drive end-to-end implementation and deployment with extensive knowledge of Azure and AWS services Stay abreast of emerging trends, complex patterns, data dependencies, and advancements in AI architecture, contributing to the refinement and innovation of application development processes. Expertise You'll Bring: Bachelor Degree or Master’s degree (or equivalent experience) or PhD in Computer Science, Electrical Engineering, Artificial Intelligence, or Applied Math with 8+ years of experience. Excellent programming skills in Python with strong fundamentals in programming, optimizations and software design Strong knowledge of ML/DL techniques, algorithms, and tools with exposure to CNN, RNN (LSTM), Transformers (BERT, BART, GPT/T5, Megatron, LLMs) Hands-on experience on conversational AI Technologies like Natural Language Understanding, Natural Language Generation, Dialog systems (including system integration, state tracking and action prediction), Information retrieval and Question and Answering, Machine Translation etc. Experience with Training BERT, GPT and Megatron Models for different NLP and dialog system tasks using “PyTorch” Deep Learning Frameworks and performing NLP data wrangling and tokenization Understanding of MLOps life cycle and experience with MLOps workflows & traceability and versioning of datasets including knowhow of database management and queries (in SQL, MongoDB etc) Experience using end-to-end MLOps platform such as Kubeflow, MLFlow, AirFlow Strong collaborative and interpersonal skills, specifically a proven ability to effectively guide and influence within a dynamic matrix environment. Benefits: Competitive salary and benefits package Culture focused on talent development with quarterly promotion cycles and company-sponsored higher education and certifications Opportunity to work with cutting-edge technologies Employee engagement initiatives such as project parties, flexible work hours, and Long Service awards Annual health check-ups Insurance coverage: group term life, personal accident, and Mediclaim hospitalization for self, spouse, two children, and parents Values-Driven, People-Centric & Inclusive Work Environment: Persistent Ltd. is dedicated to fostering diversity and inclusion in the workplace. We invite applications from all qualified individuals, including those with disabilities, and regardless of gender or gender preference. We welcome diverse candidates from all backgrounds. We support hybrid work and flexible hours to fit diverse lifestyles. Our office is accessibility-friendly, with ergonomic setups and assistive technologies to support employees with physical disabilities. If you are a person with disabilities and have specific requirements, please inform us during the application process or at any time during your employment Let’s unleash your full potential at Persistent - persistent.com/careers “Persistent is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind.”

Job Summary We are seeking a highly skilled and experienced AI/ML Engineer to join our growing AI team. The ideal candidate will have a strong foundation in machine learning, deep learning, and data science, with hands-on experience in building scalable AI solutions using open-source tools and cloud infrastructure. You will work on cutting-edge projects involving generative AI, predictive modeling, and intelligent automation. Key Responsibilities Design, build, and deploy advanced ML models for applications such as forecasting, anomaly detection, clustering, trend analysis, and pattern recognition. Develop and optimize GenAI solutions leveraging models like GPT-3.5/4/5, LLaMA 2, Falcon, Gemini and apply prompt engineering best practices. Build and maintain basic Retrieval-Augmented Generation (RAG) pipelines. Process and analyze both structured and unstructured data from diverse sources. Implement, test, and deploy ML models using FastAPI, Flask, Docker, and similar frameworks. Conduct data preprocessing, feature engineering, and statistical analysis to prepare datasets for modeling. Collaborate with cross-functional teams to integrate models into production systems hosted on AWS (EC2, S3, ECR). Evaluate model performance using standard metrics and iterate on improvements. Required Skills And Experience 4+ years of hands-on experience in AI/ML and Data Science with a strong grasp of open-source ML/DL tools. Proficient in Python and data science libraries such as NumPy, SciPy, Scikit-learn, Matplotlib, and CUDA for GPU computing. Strong experience in at least one of the following: Time Series Analysis Standard Machine Learning Algorithms Deep Learning Architectures Hands-on experience with GenAI models and prompt engineering techniques. Working knowledge of cloud platforms, preferably AWS. Familiarity with containerization and model deployment (Docker, FastAPI, Flask). Solid understanding of statistics, model validation techniques, and evaluation metrics. Preferred Expertise (One Or More) Proficiency with object detection frameworks such as YOLO, Detectron, TFOD, ideally in a distributed computing environment. Deep knowledge of deep learning architectures like CNN, RNN, Transformers (LSTM, ResNet, etc.). Experience with NLP models and frameworks, including BERT, ELMo, GPT-2, XLNet, T5, CRFs, and ONNX. Additional Qualities Strong analytical and problem-solving skills. Ability to communicate technical concepts effectively. Proactive decision-making and a strong sense of ownership.

Job Summary: We are seeking a highly skilled and experienced AI/ML Engineer to join our growing AI team. The ideal candidate will have a strong foundation in machine learning, deep learning, and data science, with hands-on experience in building scalable AI solutions using open-source tools and cloud infrastructure. You will work on cutting-edge projects involving generative AI, predictive modeling, and intelligent automation. Key Responsibilities: Design, build, and deploy advanced ML models for applications such as forecasting, anomaly detection, clustering, trend analysis, and pattern recognition. Develop and optimize GenAI solutions leveraging models like GPT-3.5/4/5, LLaMA 2, Falcon , Gemini and apply prompt engineering best practices. Build and maintain basic Retrieval-Augmented Generation (RAG) pipelines . Process and analyze both structured and unstructured data from diverse sources. Implement, test, and deploy ML models using FastAPI, Flask, Docker , and similar frameworks. Conduct data preprocessing, feature engineering, and statistical analysis to prepare datasets for modeling. Collaborate with cross-functional teams to integrate models into production systems hosted on AWS (EC2, S3, ECR). Evaluate model performance using standard metrics and iterate on improvements. Required Skills and Experience: 4+ years of hands-on experience in AI/ML and Data Science with a strong grasp of open-source ML/DL tools. Proficient in Python and data science libraries such as NumPy, SciPy, Scikit-learn, Matplotlib , and CUDA for GPU computing. Strong experience in at least one of the following: Time Series Analysis Standard Machine Learning Algorithms Deep Learning Architectures Hands-on experience with GenAI models and prompt engineering techniques. Working knowledge of cloud platforms , preferably AWS . Familiarity with containerization and model deployment (Docker, FastAPI, Flask). Solid understanding of statistics , model validation techniques, and evaluation metrics. Preferred Expertise (One or More): Proficiency with object detection frameworks such as YOLO, Detectron, TFOD , ideally in a distributed computing environment. Deep knowledge of deep learning architectures like CNN, RNN, Transformers (LSTM, ResNet, etc.) . Experience with NLP models and frameworks , including BERT, ELMo, GPT-2, XLNet, T5, CRFs , and ONNX. Additional Qualities: Strong analytical and problem-solving skills. Ability to communicate technical concepts effectively. Proactive decision-making and a strong sense of ownership.

Designation: Assistant Vice President – Generative AI / AI-ML Experience: 15+ years (with 10+ years in AI/ML and 3+ years in Generative AI) Location: Gurugram (Work from Office) Domain: Insurance (P&C, Life, Health, or Reinsurance) Cloud Preference: Azure (AWS/GCP also acceptable) Role Overview We are seeking a dynamic Assistant Vice President – Generative AI / AI-ML to drive large-scale AI strategy, execution, and leadership within the insurance domain. The AVP will define and deliver the Generative AI roadmap, lead global teams, and design enterprise-scale AI solutions that transform underwriting, claims, fraud detection, pricing, and customer experience. The ideal candidate combines deep technical expertise, strong leadership skills, and insurance domain knowledge with the ability to engage C-level stakeholders and represent the organization as a thought leader in AI innovation. Key Responsibilities Strategy & Roadmap Define and execute the Generative AI strategy across core insurance functions (underwriting, claims, fraud, pricing, CX). Identify business opportunities where AI/ML and LLMs can create measurable impact. Leadership & Team Management Lead, mentor, and scale global teams of 50+ Data Scientists, AI/ML Engineers, and Cloud Specialists. Foster innovation through hackathons, PoCs, and capability-building programs. Architecture & Delivery Architect, design, and deliver LLM-powered solutions using OpenAI, LangChain, Falcon, Dolly, LLaMA, and similar frameworks. Oversee development and deployment of predictive, prescriptive, and generative AI models. Ensure solutions are scalable, secure, and production-ready using Azure ML, Databricks, GCP Vertex AI, MLFlow, etc. Governance & Compliance Establish and enforce AI governance, ethical AI practices, and compliance frameworks . Ensure all solutions align with regulatory and data privacy requirements. Client Engagement & Thought Leadership Collaborate with insurance leaders and clients to co-create high-value solutions. Represent the organization in executive forums, client meetings, RFPs, and industry events . Act as a thought leader, publishing insights and driving market recognition in AI for Insurance. Required Skills & Experience IT & AI Expertise 20+ years in IT, including 10+ years in AI/ML and at least 3 years in Generative AI. Strong hands-on expertise with Python, PySpark, SQL, ML/DL architectures (ANN, CNN, RNN, LSTM, GRU, AutoEncoders) . Proficiency with Generative AI frameworks (OpenAI, LangChain, ChromaDB, Falcon, Dolly, LLaMA, TensorFlow, PyTorch, Kedro). Cloud & MLOps Hands-on experience with Azure (preferred), AWS, and GCP . Strong exposure to MLOps, CI/CD pipelines, Kubernetes, DevOps tools for scalable AI delivery. Insurance Domain Proven applications of AI/ML in claims automation, fraud analytics, underwriting, and policy servicing . Business acumen to translate insurance requirements into AI-driven solutions. Leadership & Communication Experience managing large-scale programs and global AI delivery teams. Strong stakeholder management skills with the ability to engage C-suite executives . Track record of driving innovation, automation, and transformation in enterprise settings. Education & Certifications Bachelor’s Degree in Computer Science, Engineering, or related field (Master’s preferred). Project Management Professional (PMP – PMI) . Microsoft Certified: Azure Data Scientist & Azure AI Engineer . IBM-Coursera Data Science Professional . Scaled Agile (SAFe) Practitioner . Deep Learning & Advanced AI certifications (preferred).