1069 Llm Jobs - Page 41

We are seeking a proficient AI/ML Engineer responsible for testing, deploying, and hosting various Large Language Models (LLMs). The role involves monitoring deployed agentic services, ensuring their optimal performance, and staying abreast of the latest advancements in AI technologies. Key Responsibilities: LLM Deployment & Hosting Evaluate, test, and deploy diverse LLMs on cloud-based and on-premise infrastructures. Optimize model performance for scalability and efficiency. Implement secure API endpoints and model-serving pipelines. Strong CI/CD skills and knowledge of CI/CD tools like Jenkins, GitHub Actions, CircleCI etc. Agentic AI Services Deploy and maintain AI agents using frameworks such as CrewAI, Agnos(Phi Data), AutoGen etc. Integrate LLMs into business workflows and automation tools. Design, monitor, and enhance agentic services for real-world applications. Monitoring & Optimization Utilize advanced observability tools to monitor model performance, latency, and cost efficiency. Implement tools like AgentOps, OpenLIT, Langfuse, and Langtrace for comprehensive monitoring and debugging of LLM applications. Develop logging, tracing, and alerting systems for deployed models and AI agents. Conduct A/B testing and gather user feedback to refine AI behavior. Address model drift and retrain models as necessary. Best Practices & Research Stay updated with the latest advancements in AI, LLMs, and agentic systems. Implement best practices for prompt engineering, reinforcement learning from human feedback (RLHF), and fine-tuning methodologies. Optimize compute costs and infrastructure usage for AI applications. Collaborate with researchers and ML engineers to integrate state-of-the-art AI techniques. Strong knowledge on version control tools like GitHub Qualifications & Skills: Proficiency with LLM frameworks such as Hugging Face Transformers, OpenAI API, or Meta AI models. Strong programming skills in Python and experience with deep learning libraries (PyTorch, TensorFlow, JAX). Experience with cloud platforms (AWS, Azure, GCP) and model deployment tools (Docker, Kubernetes, FastAPI, Ray Serve). Familiarity with vector databases (FAISS, Pinecone, Weaviate) and retrieval-augmented generation (RAG) techniques. Hands-on experience with monitoring tools such as AgentOps, OpenLIT, Langfuse, and Langtrace. Understanding of prompt engineering, LLM fine-tuning, and agent-based automation. Excellent problem-solving skills and the ability to work in a dynamic AI research and deployment team. Preferred Qualifications: Experience in reinforcement learning, fine-tuning LLMs, or training custom models. Knowledge of security best practices for AI applications. Contributions to open-source AI/ML projects or research publications in the field.

We are seeking an experienced AI/ML Specialist with a strong background in Natural Language Processing (NLP), Large Language Models (LLM), and Text-to-Speech (TTS) technologies. The ideal candidate will have a proven track record of designing and implementing AI/ML solutions, developing NLP pipelines, fine-tuning large language models, and working with TTS systems. This role offers the opportunity to work on cutting-edge AI-driven projects that have a transformative impact across industries.

Role : Principal Data Scientist / LLM Specialist Responsibilities : Strategic Leadership :a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities.b. Oversee the design, development, and implementation of data science solutions.c. Foster a culture of innovation and continuous improvement within the data science team. Data Strategy :a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks.b. Design and implement data preprocessing, cleaning, and augmentation techniques.c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. Data-Driven ML Development :a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains.c. Apply advanced data mining and machine learning techniques to extract valuable insights.d. Evaluate ML performance using rigorous data-driven metrics. ML Data Visualization and Analysis :a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance.b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection.c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills :- 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field.- Deep understanding of LLM architectures, algorithms, and techniques.- Strong experience in Python, SQL. Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question answering is must. Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch) Experience with data labeling and annotation tools. Certifications in cloud platforms or ML technologies can be a plus. Proficient knowledge of cloud platforms (AWS, GCP, Azure). Strong problem solving and analytical skills. Ability to work in team oriented, collaborative environment.Keywords : Data Scientist Statistician Data Engineer Python R Supervised & Unsupervised learning Machine Learning Algorithms Deep Learning Hugging Face Natural Language Processing (NLP) Computer Vision TensorFlow PyTorch Scikit learn Transformers LLM Frameworks LLM Models GPT 3, 4o BERT Llama

Responsibilities : 1. Strategic Leadership : a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of data science solutions. c. Foster a culture of innovation and continuous improvement within the data science team. 2. Data Strategy : a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks. b. Design and implement data preprocessing, cleaning, and augmentation techniques. c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. 3. Data-Driven ML Development : a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains. c. Apply advanced data mining and machine learning techniques to extract valuable insights. d. Evaluate ML performance using rigorous data-driven metrics. 4. ML Data Visualization and Analysis : a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance. b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection. c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills : - 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field. - Deep understanding of LLM architectures, algorithms, and techniques. - Strong experience in Python, SQL. - Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries - Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. - Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question-answering is must. - Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch) - Experience with data labeling and annotation tools. - Certifications in cloud platforms or ML technologies can be a plus. - Proficient knowledge of cloud platforms (AWS, GCP, Azure). - Strong problem-solving and analytical skills. - Ability to work in team-oriented, collaborative environment. Keywords : - Data Scientist - Statistician - Data Engineer - Python - R - Supervised & Unsupervised learning - Machine Learning Algorithms - Deep Learning - Hugging Face - Natural Language Processing (NLP) - Computer Vision - TensorFlow - PyTorch - Scikit-learn - Transformers - LLM Frameworks - LLM Models - GPT -3, 4o - BERT - Llama

Responsibilities : 1. Strategic Leadership : a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of data science solutions. c. Foster a culture of innovation and continuous improvement within the data science team. 2. Data Strategy : a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks. b. Design and implement data preprocessing, cleaning, and augmentation techniques. c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. 3. Data-Driven ML Development : a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains. c. Apply advanced data mining and machine learning techniques to extract valuable insights. d. Evaluate ML performance using rigorous data-driven metrics. 4. ML Data Visualization and Analysis : a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance. b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection. c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills : - 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field. - Deep understanding of LLM architectures, algorithms, and techniques. - Strong experience in Python, SQL. - Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries - Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. - Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question-answering is must. - Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch) - Experience with data labeling and annotation tools. - Certifications in cloud platforms or ML technologies can be a plus. - Proficient knowledge of cloud platforms (AWS, GCP, Azure). - Strong problem-solving and analytical skills. - Ability to work in team-oriented, collaborative environment. Keywords : - Data Scientist - Statistician - Data Engineer - Python - R - Supervised & Unsupervised learning - Machine Learning Algorithms - Deep Learning - Hugging Face - Natural Language Processing (NLP) - Computer Vision - TensorFlow - PyTorch - Scikit-learn - Transformers - LLM Frameworks - LLM Models - GPT -3, 4o - BERT - Llama

Role : Principal Data Scientist / LLM Specialist Responsibilities : 1. Strategic Leadership : a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of data science solutions. c. Foster a culture of innovation and continuous improvement within the data science team. 2. Data Strategy : a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks. b. Design and implement data preprocessing, cleaning, and augmentation techniques. c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. 3. Data-Driven ML Development : a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains. c. Apply advanced data mining and machine learning techniques to extract valuable insights. d. Evaluate ML performance using rigorous data-driven metrics. 4. ML Data Visualization and Analysis : a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance. b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection. c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills : - 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field. - Deep understanding of LLM architectures, algorithms, and techniques. - Strong experience in Python, SQL. - Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries - Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. - Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question-answering is must. - Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch) - Experience with data labeling and annotation tools. - Certifications in cloud platforms or ML technologies can be a plus. - Proficient knowledge of cloud platforms (AWS, GCP, Azure). - Strong problem-solving and analytical skills. - Ability to work in team-oriented, collaborative environment. Keywords : - Data Scientist - Statistician - Data Engineer - Python - R - Supervised & Unsupervised learning - Machine Learning Algorithms - Deep Learning - Hugging Face - Natural Language Processing (NLP) - Computer Vision - TensorFlow - PyTorch - Scikit-learn - Transformers - LLM Frameworks - LLM Models - GPT -3, 4o - BERT - Llama

: Role : Principal Data Scientist / LLM Specialist Responsibilities : 1. Strategic Leadership : a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of data science solutions. c. Foster a culture of innovation and continuous improvement within the data science team. 2. Data Strategy : a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks. b. Design and implement data preprocessing, cleaning, and augmentation techniques. c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. 3. Data-Driven ML Development : a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains. c. Apply advanced data mining and machine learning techniques to extract valuable insights. d. Evaluate ML performance using rigorous data-driven metrics. 4. ML Data Visualization and Analysis : a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance. b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection. c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills : - 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field. - Deep understanding of LLM architectures, algorithms, and techniques. - Strong experience in Python, SQL. - Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries - Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. - Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question-answering is must. - Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch) - Experience with data labeling and annotation tools. - Certifications in cloud platforms or ML technologies can be a plus. - Proficient knowledge of cloud platforms (AWS, GCP, Azure). - Strong problem-solving and analytical skills. - Ability to work in team-oriented, collaborative environment. Keywords : - Data Scientist - Statistician - Data Engineer - Python - R - Supervised & Unsupervised learning - Machine Learning Algorithms - Deep Learning - Hugging Face - Natural Language Processing (NLP) - Computer Vision - TensorFlow - PyTorch - Scikit-learn - Transformers - LLM Frameworks - LLM Models - GPT -3, 4o - BERT - Llama

You'll be Responsible for: Design, develop and implement cutting-edge AI/ML solutions, including Large Language Models (LLMs) and Generative AI applications Lead projects end-to-end while mentoring team members in AI-ML, including traditional ML and emerging AI technologies Drive innovation in AI agent development and orchestration for automated decision-making systems Establish best practices for responsible AI development and deployment What you'd have: AI-ML Engineer or Data Scientist with 3 11 years of relevant experience Strong expertise in modern AI frameworks and tools: ML/DL frameworks (TensorFlow, PyTorch, Keras, Sklearn) LLM frameworks (LangChain, LlamaIndex, CrewAI, AutoGen) Vector databases (Pinecone, Weaviate, ChromaDB) – Good To Have Hands-on experience with: Generative AI and foundation models Prompt engineering and LLM fine-tuning AI agent development and orchestration RAG (Retrieval-Augmented Generation) systems Proven experience in data preparation, including: Advanced data preprocessing techniques Feature engineering Data quality assessment and improvement Proficiency in data exploration tools (Pandas, Numpy, Polars) Strong programming skills in Python; R is a plus Deep understanding of: Statistics and probability ML/DL algorithms AI governance and responsible AI practices Experience with: Large-scale data processing Model deployment and MLOps Cost optimization for AI systems Track record of 3-5 successful AI/ML projects in production Excellent communication skills and team leadership ability Continuous learning mindset for emerging AI technologies Skills: Statistics and Mathematics Classical Machine Learning Deep Learning and Neural Networks Generative AI and LLMs Agentic AI Development MLOps and Production Engineering Data Engineering Model Optimization and Tuning AI Governance and Ethics Team Leadership Why join us? Impactful Work: Drive innovation in AI technologies while safeguarding Tanla's assets, data, and reputation Growth Opportunities: Be part of a rapidly growing company in the telecom and CPaaS space, with clear paths for professional advancement Innovative Environment: Work with a world-class team on cutting-edge AI technologies in a challenging and collaborative environment Research Opportunities: Contribute to the advancement of GenAI and Agentic AI technologies Tanla is an equal opportunity employer. We champion diversity and are committed to creating an inclusive environment for all employees. www.tanla.com

The Role : The ideal candidate for this role will be an innovative self-starter. You will be an AI expert with experience in making architectural tradeoffs to transform AI performance for a variety of use cases. You will collaborate with internal and external development engineers (architecture, hardware, validation, software services). You will contribute to development, support device characterization and benchmarking efforts. Responsibilities : - Proficiency in Large Models & Deep Neural Networks. - Hands-on experience in working with large models & deep neural networks. - Expertise in LLMs with working knowledge of large language models (LLMs). - Extensive experience in System platform Architecture. - Experience in Development Preferable for memory/storage/ any embedded system. - In depth knowledge and extensive experience in dealing with Standardizations/Technical Papers/Patents. - Extensive experience with C/C++ and Python programming. - Develop and fine-tune LLMs (GPT, Llama, Mistral, Falcon, Claude, etc.) for domain-specific applications. - Implement RAG pipelines using LlamaIndex, LangGraph, and vector databases (FAISS, Pinecone, Weaviate, ChromaDB) to enhance response accuracy. - Build AI-powered chatbots and autonomous agents using LangGraph, CrewAI, LlamaIndex, and OpenAI APIs. - Optimize and deploy generative AI models for real-time inference using cloud platforms (AWS, GCP, Azure) and MLOps tools (Docker, Kubernetes, MLflow). - Fine-tune models using LoRA, QLoRA, PEFT, and RLHF to improve efficiency and personalization. - Develop AI-driven workflows for structured reasoning and decision-making using CrewAI and LangGraph. - Integrate multi-modal AI models (text, image, speech) into enterprise solutions. - Implement memory and retrieval strategies for LLM-based systems using vector search and caching techniques. - Ensure AI models follow ethical AI guidelines, bias mitigation, and security best practices. Requirement : - Tech in Computer Science, Electrical Engineering. - 8 to 10 Years of experience in relevant domain. - Strong analytical & abstract thinking ability as well as technical communication skills. - Able to work independently and perform in fast paced environment. - Ability to troubleshoot and debug complex issues. - Working Knowledge on Device Driver is desirable. - Prior experience in working with Agile/Scrum. Required Skills : - Languages & Frameworks : Proficiency in Python, PyTorch, TensorFlow, JAX.

Responsibilities : 1. Strategic Leadership : a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of data science solutions. c. Foster a culture of innovation and continuous improvement within the data science team. 2. Data Strategy : a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks. b. Design and implement data preprocessing, cleaning, and augmentation techniques. c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. 3. Data-Driven ML Development : a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains. c. Apply advanced data mining and machine learning techniques to extract valuable insights. d. Evaluate ML performance using rigorous data-driven metrics. 4. ML Data Visualization and Analysis : a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance. b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection. c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills : - 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field. - Deep understanding of LLM architectures, algorithms, and techniques. - Strong experience in Python, SQL. - Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries - Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. - Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question-answering is must. - Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch) - Experience with data labeling and annotation tools. - Certifications in cloud platforms or ML technologies can be a plus. - Proficient knowledge of cloud platforms (AWS, GCP, Azure). - Strong problem-solving and analytical skills. - Ability to work in team-oriented, collaborative environment. Keywords : - Data Scientist - Statistician - Data Engineer - Python - R - Supervised & Unsupervised learning - Machine Learning Algorithms - Deep Learning - Hugging Face - Natural Language Processing (NLP) - Computer Vision - TensorFlow - PyTorch - Scikit-learn - Transformers - LLM Frameworks - LLM Models - GPT -3, 4o - BERT - Llama.

Role & responsibilities esponsibilities 1Design develop test and implement AI and ML models using the latest technologies and frameworks 2Analyze and interpret complex data to identify trends patterns and insights 3Work with AI lead to understand business objectives and requirements and develop solutions to solve complex problems 4Maintain uptodate knowledge of latest AIML trends techniques and technologies 5Collaborate with data engineers to build data and model pipelines 6Conduct research and implement new methodologies in AI and ML ESSENTIAL SKILLS COMPETENCIES Minimum 5to 6 years of experience in implementing AI and ML models Excellent problemsolving skills with the ability to work independently as well as collaboratively in a team Solid understanding of machine learning algorithms and principles and their applicability to different problems Proficiency in Python and familiarity with libraries like TensorFlow Keras PyTorch or similar ML libraries Experience with GenAI would be highly beneficial Strong communication and presentation skills Skills Mandatory Skills : Prompt Engineering & RAG Good to Have Skills : Fine Tuning Large Language Models Preferred candidate profile

Senior Software Engineer/ LLM Ops Engineer External Description Description - External JD - What You Will Do Design, implement, and maintain LLM operations workflows using tools like Langfuse to monitor performance, track usage, and create feedback loops for continuous improvement Develop and maintain infrastructure-as-code for AI deployments using Terraform and AWS services (Lambda, SQS, API Gateway, OpenSearch, CloudWatch) Build and enhance monitoring, logging, and alerting systems to ensure optimal performance and reliability of our LLM infrastructure Collaborate with AI engineers to design and implement evaluation frameworks (including LLM-as-judge systems) to measure and improve model performance Manage prompt versioning, testing, and deployment pipelines through CI/CD and custom tooling Implement and maintain security guardrails for LLM interactions, ensuring compliance with best practices Create comprehensive documentation for LLM operations, including runbooks for production incidents Participate in on-call rotations to support mission-critical AI systems Drive innovation in LLM operations by researching and implementing best practices and emerging tools in the rapidly evolving GenAI space Deep understanding of prompt engineering strategies What You Will Bring To succeed in this role, you will need a combination of experience, technology skills, personal qualities, and education. Required Qualifications 3+ years of experience in DevOps, SRE, or similar roles, with at least 1 year specifically working with LLMs or AI systems in production Strong hands-on experience with AWS cloud services, particularly Bedrock, Lambda, SQS, API Gateway, OpenSearch, and CloudWatch Experience with infrastructure-as-code using Terraform, CloudFormation, or similar tools Proficiency in Python and experience building automation tooling and pipelines Familiarity with LangOps platforms such as Langfuse for LLM observability and evaluation Experience with CI/CD pipelines Knowledge of logging, monitoring, and alerting systems Understanding of security best practices for AI systems, including prompt injection mitigation techniques Excellent troubleshooting and problem-solving skills Strong communication skills and ability to work effectively with cross-functional teams Must be legally entitled to work in the country where the role is located Preferred Qualifications Experience with prompt engineering and testing tools like Promptfoo Familiarity with vector databases and retrieval-augmented generation (RAG) systems Knowledge of serverless architectures and event-driven systems Experience with AWS Guardrails for LLM security Background in data engineering or machine learning operations Understanding of financial systems and data security requirements in the finance industry Familiarity with implementing technical solutions to meet compliance requirements outlined in SOC2, ISAE 3402, and ISO 27001

We are seeking an experienced and innovative AI Specialist to join our dynamic team. The ideal candidate will possess deep expertise in artificial intelligence, machine learning, and neural networks, with a proven track record of designing, developing, and deploying AI solutions. The candidate should be adept at analyzing large datasets, developing optimized algorithms, and integrating AI models into production environments. This role offers the opportunity to contribute to cutting-edge AI applications that solve real-world problems and drive business value Key Responsibilities: AI System Design & Development: Design, develop, and implement AI systems and applications, including machine learning models, neural networks, and natural language processing (NLP) algorithms. Create and optimize AI algorithms for tasks such as image recognition, speech processing, and predictive analytics. Data Analysis & Feature Engineering: Analyze large datasets to identify trends, patterns, and actionable insights. Perform data preprocessing, data wrangling, and feature engineering to enhance model performance. Model Training & Optimization: Train and fine-tune machine learning models using labeled and unlabeled data to improve model accuracy and efficiency. Continuously monitor and optimize models for better performance. AI Model Integration: Deploy machine learning models into production environments, ensuring scalability and reliability. Integrate AI technologies with existing software and business processes using REST APIs. Utilize Docker and Kubernetes to containerize and manage AI models. Research & Innovation: Stay up to date with the latest advancements in AI and machine learning technologies. Explore and experiment with new AI applications, tools, and methodologies to enhance system capabilities. Collaboration & Agile Development: Work closely with cross-functional teams in an Agile environment to develop AI-driven solutions that meet business requirements. Communicate findings and results to stakeholders, ensuring AI solutions align with business goals. Primary Skills and Qualifications: AI and Machine Learning Expertise: Strong knowledge of supervised and unsupervised learning techniques, including classification, regression, clustering, and reinforcement learning. Hands-on experience developing and deploying machine learning models in production environments. Technical Proficiency: Programming Languages: Advanced proficiency in Python, with additional experience in R and SQL as a plus. ML Libraries/Frameworks: Expertise in TensorFlow, PyTorch, and scikit-learn. Cloud Platforms: Hands-on experience with AWS, Azure, or Google Cloud for model deployment and management. Model Deployment & Integration: Familiarity with REST APIs, Docker, and Kubernetes for seamless model integration. Secondary Skills and Preferred Qualifications: Natural Language Processing (NLP): Experience with NLP frameworks such as NLTK, spaCy, Hugging Face, and BERT. Agile Methodologies: Knowledge of Agile practices, with experience using tools like JIRA for project management. Mathematics and Statistics: Strong foundation in probability, statistics, and linear algebra to support model development and evaluation. Data Handling & Preprocessing: Proficiency in data cleaning, transformation, and feature engineering to prepare data for modeling.

Responsibilities : - Design and Development : Architect, design, and develop user-friendly and scalable AI-powered applications using React.js for the frontend and Node.js for the backend. - Generative AI Implementation : Implement, integrate, and fine-tune Generative AI models (e.g., text generation, image synthesis) for specific application requirements. - API Development and Integration : Develop and consume RESTful and GraphQL APIs to facilitate communication between frontend applications and AI/ML models and backend services. - LLM and NLP Integration : Leverage Large Language Models (LLMs) and Natural Language Processing (NLP) techniques to enhance application features and user interactions. - Machine Learning Frameworks : Utilize and integrate various machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn) as needed for specific AI functionalities. - Performance Optimization : Optimize AI models and application code for performance, scalability, reliability, and cost-effectiveness. - Collaboration : Collaborate effectively with data scientists, machine learning engineers, backend developers, product managers, and designers throughout the development lifecycle. - Code Quality and Best Practices : Write clean, well-documented, and testable code, adhering to best practices and contributing to CI/CD pipelines for continuous integration and deployment. - Research and Innovation : Stay abreast of the latest advancements in Generative AI, Large Language Models, NLP, web development technologies, and industry trends. Proactively explore and propose new technologies and approaches to enhance our products. - Problem Solving : Troubleshoot and resolve technical issues related to AI model integration, application performance, and API interactions. - Documentation : Create and maintain comprehensive technical documentation for developed components and systems. Qualifications : - Education : Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field. - Experience : [Specify the desired years of experience - e.g., 2-5 years] of professional experience in full-stack development with a focus on frontend (React.js) and backend (Node.js) technologies. - Frontend Expertise : Proven experience in developing responsive and dynamic user interfaces using React.js, including state management libraries (e.g., Redux, Zustand, Context API), component-based architecture, and modern JavaScript (ES6+). - Backend Expertise : Solid experience in developing robust and scalable backend applications using Node.js and frameworks like Express.js or NestJS. - API Development : Strong understanding and practical experience in designing and implementing RESTful and/or GraphQL APIs. - AI/ML Fundamentals : Familiarity with fundamental concepts of Artificial Intelligence and Machine Learning, including different types of models and their applications. - Generative AI Exposure : Experience with implementing or fine-tuning Generative AI models (e.g., using libraries or cloud-based services). - LLM and NLP Knowledge : Understanding of Large Language Models (LLMs) and Natural Language Processing (NLP) concepts and their practical applications. - Machine Learning Frameworks (Desirable) : Familiarity with one or more machine learning frameworks such as TensorFlow, PyTorch, or scikit-learn. - Database Knowledge : Experience working with various database systems (e.g., PostgreSQL, MySQL, MongoDB). - DevOps Practices : Understanding of CI/CD pipelines and experience with relevant tools (e.g., Jenkins, GitLab CI/CD, GitHub Actions). - Testing : Experience with writing unit, integration, and end-to-end tests. - Problem-Solving Skills : Excellent analytical and problem-solving abilities. - Communication Skills : Strong written and verbal communication skills, with the ability to effectively communicate technical concepts to both technical and non-technical audiences. - Collaboration : Ability to work effectively in a collaborative team environment.

Responsibilities : 1. Strategic Leadership :a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities.b. Oversee the design, development, and implementation of data science solutions.c. Foster a culture of innovation and continuous improvement within the data science team. 2. Data Strategy :a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks.b. Design and implement data preprocessing, cleaning, and augmentation techniques.c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. 3. Data-Driven ML Development :a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains.c. Apply advanced data mining and machine learning techniques to extract valuable insights.d. Evaluate ML performance using rigorous data-driven metrics. 4. ML Data Visualization and Analysis :a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance.b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection.c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills :- 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field.- Deep understanding of LLM architectures, algorithms, and techniques.- Strong experience in Python, SQL. - Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries- Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. - Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question-answering is must.- Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch)- Experience with data labeling and annotation tools.- Certifications in cloud platforms or ML technologies can be a plus.- Proficient knowledge of cloud platforms (AWS, GCP, Azure).- Strong problem-solving and analytical skills.- Ability to work in team-oriented, collaborative environment. Keywords :- Data Scientist- Statistician- Data Engineer- Python- R- Supervised & Unsupervised learning- Machine Learning Algorithms- Deep Learning- Hugging Face- Natural Language Processing (NLP)- Computer Vision- TensorFlow- PyTorch- Scikit-learn- Transformers- LLM Frameworks- LLM Models- GPT -3, 4o- BERT- Llama

Responsibilities : 1. Strategic Leadership :a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities.b. Oversee the design, development, and implementation of data science solutions.c. Foster a culture of innovation and continuous improvement within the data science team. 2. Data Strategy :a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks.b. Design and implement data preprocessing, cleaning, and augmentation techniques.c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. 3. Data-Driven ML Development :a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains.c. Apply advanced data mining and machine learning techniques to extract valuable insights.d. Evaluate ML performance using rigorous data-driven metrics. 4. ML Data Visualization and Analysis :a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance.b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection.c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills :- 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field.- Deep understanding of LLM architectures, algorithms, and techniques.- Strong experience in Python, SQL. - Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries- Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. - Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question-answering is must.- Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch)- Experience with data labeling and annotation tools.- Certifications in cloud platforms or ML technologies can be a plus.- Proficient knowledge of cloud platforms (AWS, GCP, Azure).- Strong problem-solving and analytical skills.- Ability to work in team-oriented, collaborative environment. Keywords :- Data Scientist- Statistician- Data Engineer- Python- R- Supervised & Unsupervised learning- Machine Learning Algorithms- Deep Learning- Hugging Face- Natural Language Processing (NLP)- Computer Vision- TensorFlow- PyTorch- Scikit-learn- Transformers- LLM Frameworks- LLM Models- GPT -3, 4o- BERT- Llama

Responsibilities : 1. Strategic Leadership : a. Define and drive the overall data science strategy and roadmap for the organization, aligning it with business objectives and technical capabilities. b. Oversee the design, development, and implementation of data science solutions. c. Foster a culture of innovation and continuous improvement within the data science team. 2. Data Strategy : a. Develop and deploy advanced data science models and algorithms, including LLMs, to solve complex business problems. Identify, acquire, and curate high-quality datasets relevant ML tasks. b. Design and implement data preprocessing, cleaning, and augmentation techniques. c. Leverage LLMs to extract deeper insights from data, including unstructured text and natural language data. 3. Data-Driven ML Development : a. Collaborate with LLM experts to integrate LLMs into data science pipelines and applications. b. Optimize LLM performance for specific tasks and domains. c. Apply advanced data mining and machine learning techniques to extract valuable insights. d. Evaluate ML performance using rigorous data-driven metrics. 4. ML Data Visualization and Analysis : a. Develop data visualizations and analysis techniques to understand ML Al behavior and performance. b. Identify trends, patterns, and anomalies in data. Explore new applications of LLMs in data science, such as LLM-powered recommendation systems, predictive analytics, and anomaly detection. c. Communicate data-driven insights to stakeholders effectively. Minimum Required Skills : - 11+ years of experience preferred. - Advanced degree in computer science, data science, or a related field. - Deep understanding of LLM architectures, algorithms, and techniques. - Strong experience in Python, SQL. - Should be extremely comfortable with Numpy, Pandas, Matplotlib and Scikit learn python libraries - Strong understanding of machine learning concepts and algorithms. Should know deep learning techniques such as clustering, decision trees, random forest, etc. - Knowledge on text classification, sentiment analysis, named entity recognition, machine translation, text summarization and question-answering is must. - Experience with LLM frameworks (Hugging Face Transformers, TensorFlow, PyTorch) - Experience with data labeling and annotation tools. - Certifications in cloud platforms or ML technologies can be a plus. - Proficient knowledge of cloud platforms (AWS, GCP, Azure). - Strong problem-solving and analytical skills. - Ability to work in team-oriented, collaborative environment. Keywords : - Data Scientist - Statistician - Data Engineer - Python - R - Supervised & Unsupervised learning - Machine Learning Algorithms - Deep Learning - Hugging Face - Natural Language Processing (NLP) - Computer Vision - TensorFlow - PyTorch - Scikit-learn - Transformers - LLM Frameworks - LLM Models - GPT -3, 4o - BERT - Llama

We are looking for an AI & Machine Learning Lead Engineer to lead the development and deployment of advanced AI and machine learning models. The ideal candidate will have a strong background in precision learning, large language models (LLMs), retrieval-augmented generation (RAG), natural language processing (NLP), and statistical methods for output analysis. This role involves leading a team of engineers and data scientists, setting strategic directions for AI/ML initiatives, and ensuring the delivery of impactful solutions that align with our business objectives. Key Responsibilities: Leadership and Strategy: Lead and manage a team of AI and machine learning engineers, providing technical guidance and mentorship. Oversee the entire machine learning lifecycle, from data collection and preprocessing to model training, evaluation, and deployment. Develop and drive the AI/ML strategy, aligning it with overall business goals. Collaborate with cross-functional teams, including data engineering, product management, and software development, to integrate ML models into production environments. Precision Learning and Large Language Models (LLMs): Design, develop, and optimize precision learning algorithms for specific business applications. Lead efforts in developing, fine-tuning, and deploying large language models (LLMs) to address various use cases such as text generation, summarization, translation, and conversational AI. Retrieval-Augmented Generation (RAG): Implement and optimize RAG systems to improve the performance and accuracy of AI solutions. Develop retrieval strategies that effectively integrate large-scale knowledge bases with LLMs to generate more accurate and contextually relevant outputs. Natural Language Processing (NLP): Develop and implement NLP models for tasks such as text classification, sentiment analysis, named entity recognition, summarization, and question-answering. Stay current with NLP research trends and advancements, implementing best practices to enhance model efficiency and performance. Output Analysis through Statistical Methods: Analyse model outputs using advanced statistical methods to ensure reliability, accuracy, and explainability. Implement A/B testing, hypothesis testing, and other statistical techniques to validate model performance and derive actionable insights. Research and Development: Stay abreast of the latest research in machine learning, deep learning, and AI. Propose and implement novel approaches to solve challenging problems. Collaborate with academic and research institutions to contribute to the machine learning community through publications, open-source projects, and conferences MLOps and Model Deployment: Collaborate with DevOps and data engineering teams to implement MLOps practices for CI/CD pipelines, model monitoring, and governance. Ensure scalable deployment of AI/ML models on cloud platforms (AWS, GCP, Azure) or on-premises environments. Mentorship and Team Leadership: Mentor and guide a team of machine learning engineers and data scientists, fostering a culture of continuous learning and innovation. Conduct regular code reviews, provide constructive feedback, and ensure adherence to best practices in machine learning. Required Qualifications: 8-10 years Experience with Bachelors or Masters degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field; PhD is a plus. 5+ years of experience in machine learning and AI, with a strong focus on NLP, LLMs, RAG, and precision learning. Proven track record of leading AI/ML teams and projects from conception to deployment. Expertise in Python and relevant ML libraries/frameworks such as TensorFlow, PyTorch, scikit-learn, and Hugging Face Transformers. Strong understanding of NLP techniques, including transformer architectures (e.g., BERT, GPT). Experience in RAG techniques, knowledge retrieval systems, and integrating LLMs with external data sources. Proficiency in statistical analysis and hypothesis testing, with a strong foundation in experimental design. Excellent problem-solving skills, with the ability to articulate complex technical concepts to non-technical stakeholders. Preferred Qualifications: Experience with cloud services (AWS, GCP, Azure) for model deployment and scaling. Familiarity with MLOps practices, including model versioning, monitoring, and CI/CD pipelines for ML. Knowledge of advanced AI techniques such as reinforcement learning, meta-learning, and unsupervised learning. Strong publication record or contributions to the AI/ML community.

Summary of the Role: We are seeking an AI Prompt Engineer with expertise in Large Language Models (LLMs) and prompt engineering to support legal tech solutions, specifically in contract lifecycle management and contract-related AI implementations. The ideal candidate will have a strong background in AI, NLP, and structured prompt engineering, with a keen understanding of legal tech applications. Prior experience in the legal domain is a plus but not mandatory. What you will do: Design, develop, and refine AI prompts for legal tech applications, ensuring accuracy and contextual relevance in contract-related workflows. Work with Large Language Models (LLMs) to enhance AI-driven solutions for contract analysis, review, and automation Optimize prompt structures and techniques to improve AI-generated outputs in contract drafting, compliance, and negotiations Research, test, and implement best practices in prompt engineering to maximize efficiency and accuracy in contract delivery Evaluate and refine AI-generated legal documents to align with compliance standards and client requirements Stay updated with advancements in LLMs, prompt engineering methodologies, and AI-driven legal tech solutions. What you bring: Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field. Minimum 5 years of overall professional experience, with at least 2 years in AI prompt engineering Strong understanding of LLMs (GPT, Claude, Gemini, Llama, etc.) and their application in legal or enterprise use cases Proven expertise in prompt design, optimization, and fine-tuning for AI-driven text generation Hands-on experience with AI tools, frameworks, and APIs (OpenAI, Hugging Face, LangChain, etc.) Strong problem-solving skills and ability to work in a fast-paced environment Excellent communication and collaboration skills to work with cross-functional teams. Bonus Points: Basic understanding of contracts, legal terminology, and compliance standards. Applications must be submitted exclusively through Execo's official job postings located on the following platforms: Execo Careers Website: https://www.execo.com/careers LinkedIn: https://www.linkedin.com/company/execogroup/jobs/ Indeed: US & Kenya: https://www.indeed.com/cmp/Execo-Group-Inc India: https://in.indeed.com/cmp/Execo-Group-Inc UK: https://uk.indeed.com/cmp/Execo-Group-Inc Philippines: https://ph.indeed.com/cmp/Execo-Group-Inc Singapore: https://sg.indeed.com/cmp/Execo-Group-Inc Naukri: https://www.naukri.com/

Job Summary Good Experience on Python scripting Experience on any web framework like Tornadofastapi Experience implementing AIML and Generative AI usecases RAG implementations Experience working on Embedding and Transformer models Experience working on document processing ETL pipelines using Python Experience working on Azure AI Search Self motivated fast learner individual contributor preferred Responsibilities Good Experience on Python scripting Experience on any web framework like Tornadofastapi Experience implementing AIML and Generative AI usecases RAG implementations Experience working on Embedding and Transformer models Experience working on document processing ETL pipelines using Python Experience working on Azure AI Search Self motivated fast learner individual contributor preferred

We are looking for a Prompt Engineer with over 3 years of experience in designing, developing, and optimizing prompts for large language models (LLMs) like OpenAI GPT, Claude, or Gemini. You will work closely with data scientists, product managers, and software engineers to create effective natural language prompts that enable AI systems to perform complex tasks reliably. Key Responsibilities Design, test, and refine effective prompts for tasks involving language generation, summarization, classification, extraction, reasoning, and conversation. Evaluate and analyze LLM output for accuracy, relevance, coherence, and safety. Collaborate with cross-functional teams to integrate LLM capabilities into production applications or internal tools. Build prompt libraries and maintain versioning and documentation. Conduct prompt A/B testing, performance benchmarking, and maintain feedback loops for iterative improvements. Fine-tune or guide model behavior using advanced techniques like few-shot prompting, chain-of-thought reasoning, and system instructions. Stay updated with LLM research advancements and integrate best practices into workflow. Required Skills & Qualifications Bachelor's degree in Computer Science, Linguistics, Cognitive Science, or a related field. 3+ years of experience working with AI/ML systems or NLP-focused products. Deep understanding of prompt engineering principles and hands-on experience with LLMs like OpenAI GPT (ChatGPT, GPT-4), Anthropic Claude, etc. Familiarity with API-based interaction with LLMs (e.g., OpenAI, Azure OpenAI, Hugging Face). Strong analytical and critical thinking skills to design experiments and evaluate model behavior. Proficient in Python or JavaScript for scripting and experimentation. Excellent written and verbal communication skills. Preferred Qualifications Experience with vector databases (e.g., Pinecone, Weaviate), RAG systems, or knowledge graphs. Exposure to tools like LangChain, LlamaIndex, or PromptLayer. Experience working in agile environments or AI-driven startups. Prior work in conversational AI, chatbots, or AI copilots.

Senior LLM Engineer GenAI / ML (Python, Langchain) Full Time Location: India Overall Experience: 7-12 Years Focus: Hands-on engineering role focused on designing, building, and deploying Generative AI and LLM-based solutions. The role requires deep technical proficiency in Python and modern LLM frameworks with the ability to contribute to roadmap development and cross-functional collaboration. Key Responsibilities: Design and develop GenAI/LLM-based systems using tools such as Langchain and Retrieval-Augmented Generation (RAG) pipelines. Implement prompt engineering techniques and agent-based frameworks to deliver intelligent, context-aware solutions. Collaborate with the engineering team to shape and drive the technical roadmap for LLM initiatives. Translate business needs into scalable, production-ready AI solutions. Work closely with business SMEs and data teams to ensure alignment of AI models with real-world use cases. Contribute to architecture discussions, code reviews, and performance optimization. Skills Required: Proficient in Python, Langchain, and SQL. Understanding of LLM internals, including prompt tuning, embeddings, vector databases, and agent workflows. Background in machine learning or software engineering with a focus on system-level thinking. Experience working with cloud platforms like AWS, Azure, or GCP. Ability to work independently while collaborating effectively across teams. Excellent communication and stakeholder management skills. Preferred Qualifications: 1+ years of hands-on experience in LLMs and Generative AI techniques. Experience contributing to ML/AI product pipelines or end-to-end deployments. Familiarity with MLOps and scalable deployment patterns for AI models. Prior exposure to client-facing projects or cross-functional AI teams.

As a Prompt Engineer , you will play a crucial role in designing and refining the conversational prompts that power our AI voice agents. You will work closely with AI researchers, product managers, and developers

Role & responsibilities Main Skills : AI Archt (Solutions/Technical) - LLM/Gen AI + Any Cloud + Python Project Role : AI / ML Engineer Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing. Must have skills : Natural Language Processing (NLP) Roles & Responsibilities: - Expected to perform independently and become an SME. - Required active participation/contribution in team discussions. - Contribute in providing solutions to work related problems. - Develop applications and systems utilizing AI tools and Cloud AI services. - Apply GenAI models as part of the solution. - Implement deep learning and neural networks. - Create chatbots and work on image processing. - Collaborate with team members to provide innovative solutions. Professional & Technical Skills: - Must To Have Skills: Proficiency in Natural Language Processing (NLP). - Strong understanding of statistical analysis and machine learning algorithms. - Experience with data visualization tools such as Tableau or Power BI. - Hands-on implementing various machine learning algorithms such as linear regression, logistic regression, decision trees, and clustering algorithms. - Solid grasp of data munging techniques, including data cleaning, transformation, and normalization to ensure data quality and integrity. Additional Information: - The candidate should have a minimum of 2 years of experience in Natural Language Processing (NLP).

Job Description: We are looking for a talented and experienced Senior Data Scientist with a minimum of 4 years of professional experience in model development and expertise in Gen AI projects. As a Senior Data Scientist, you will be responsible for developing advanced machine learning models, conducting exploratory data analysis (EDA), performing feature selection/reduction, and utilizing cutting-edge technologies to deliver high-quality solutions. The ideal candidate should possess strong programming proficiency in Python and experience with cloud platforms like GCP or equivalent, as well as visualization tools like Qlik Sense, Power BI, Looker Studio, or Tableau. Job Responsibility: Data Scientists 3+ Year (Mandatory Skills) Model development (regression and classification), should have strong experience in performing EDA, Feature selection/Feature Reduction, building models and evaluate its performance Should have worked on Gen AI project Strong programming proficiency in python with mastery of data science libraries Pandas, numpy and scikitlearn & xgboost or pytorch GCP (BQ, Vertex AI) or other equivalent cloud Visualization (Qlik Sense / Looker Studio / Power BI / Tableau) Skills : GenAI & RAG Model development (regression and classification) Machine Learning Python GCP / other equivalent cloud Visualization LLM Model building We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status. Interview Date: 17th May 2025 (Saturday), Face to Face In person Interview Chennai.