39 Deploying Models Jobs

job requisition idJR1027361 Job Summary Synechron seeks a highly skilled AI/ML Engineer specializing in Natural Language Processing (NLP), Large Language Models (LLMs), Foundation Models (FMs), and Generative AI (GenAI). The successful candidate will design, develop, and deploy advanced AI solutions, contributing to innovative projects that transform monolithic systems into scalable microservices integrated with leading cloud platforms such as Azure, Amazon Bedrock, and Google Gemini. This role plays a critical part in advancing Synechrons capabilities in cutting-edge AI technologies, enabling impactful business insights and product innovations. Software Required Proficiency: Python (core librariesTensorFlow, PyTorch, Hugging Face transformers, etc.) Cloud platformsAzure, AWS, Google Cloud (familiarity with AI/ML services) ContainerizationDocker, Kubernetes Version controlGit Data management toolsSQL, NoSQL databases (e.g., MongoDB) Model deployment and MLOps toolsMLflow, CI/CD pipelines, monitoring tools Preferred Skills: Experience with cloud-native AI frameworks and SDKs Familiarity with AutoML tools Additional programming languages (e.g., Java, Scala) Overall Responsibilities Design, develop, and optimize NLP models, including advanced LLMs and Foundation Models, for diverse business use cases. Lead the development of large data pipelines for training, fine-tuning, and deploying models on big data platforms. Architect, implement, and maintain scalable AI solutions in line with MLOps best practices. Transition legacy monolithic AI systems into modular, microservices-based architectures for scalability and maintainability. Build end-to-end AI applications from scratch, including data ingestion, model training, deployment, and integration. Implement retrieval-augmented generation techniques for enhanced context understanding and response accuracy. Conduct thorough testing, validation, and debugging of AI/ML models and pipelines. Collaborate with cross-functional teams to embed AI capabilities into customer-facing and enterprise products. Support ongoing maintenance, monitoring, and scaling of deployed AI systems. Document system designs, workflows, and deployment procedures for compliance and knowledge sharing. Performance Outcomes: Production-ready AI solutions delivering high accuracy and efficiency. Robust data pipelines supporting training and inference at scale. Seamless integration of AI models with cloud infrastructure. Effective collaboration leading to innovative AI product deployment. Technical Skills (By Category) Programming Languages: Essential: Python (TensorFlow, PyTorch, Hugging Face, etc.) Preferred: Java, Scala Databases/Data Management: SQL (PostgreSQL, MySQL), NoSQL (MongoDB, DynamoDB) Cloud Technologies: Azure AI, AWS SageMaker, Bedrock, Google Cloud Vertex AI, Gemini Frameworks and Libraries: Transformers, Keras, scikit-learn, XGBoost, Hugging Face engines Development Tools & Methodologies: Docker, Kubernetes, Git, CI/CD pipelines (Jenkins, Azure DevOps) Security & Compliance: Knowledge of data security standards and privacy policies (GDPR, HIPAA as applicable) Experience 8 to 10 years of hands-on experience in AI/ML development, especially NLP and Generative AI. Demonstrated expertise in designing, fine-tuning, and deploying LLMs, FMs, and GenAI solutions. Proven ability to develop end-to-end AI applications within cloud environments. Experience transforming monolithic architectures into scalable microservices. Strong background with big data processing pipelines. Prior experience working with cloud-native AI tools and frameworks. Industry experience in finance, healthcare, or technology sectors is advantageous. Alternative Experience: Candidates with extensive research or academic experience in AI/ML, especially in NLP and large-scale data processing, are eligible if they have practical deployment experience. Day-to-Day Activities Develop and optimize sophisticated NLP/GenAI models fulfilling business requirements. Lead data pipeline construction for training and inference workflows. Collaborate with data engineers, architects, and product teams to ensure scalable deployment. Conduct model testing, validation, and performance tuning. Implement and monitor model deployment pipelines, troubleshoot issues, and improve system robustness. Document models, pipelines, and deployment procedures for audit and knowledge sharing. Stay updated with emerging AI/ML trends, integrating best practices into projects. Present findings, progress updates, and technical guidance to stakeholders. Qualifications Bachelors degree in Computer Science, Data Science, or related field; Masters or PhD preferred. Certifications in AI/ML, Cloud (e.g., AWS, Azure, Google Cloud), or Data Engineering are a plus. Proven professional experience with advanced NLP and Generative AI solutions. Commitment to continuous learning to keep pace with rapidly evolving AI technologies. Professional Competencies Strong analytical and problem-solving capabilities. Excellent communication skills, capable of translating complex technical concepts. Collaborative team player with experience working across global teams. Adaptability to rapidly changing project scopes and emerging AI trends. Innovation-driven mindset with a focus on delivering impactful solutions. Time management skills to prioritize and manage multiple projects effectively.

job requisition idJR1027352 Job Summary Synechron is seeking an analytical and innovative Senior Data Scientist to support and advance our data-driven initiatives. The ideal candidate will have a solid understanding of data science principles, hands-on experience with AI/ML tools and techniques, and the ability to interpret complex data sets to deliver actionable insights. This role contributes to the organizations strategic decision-making and technology innovation by applying advanced analytics and machine learning models in a collaborative environment. Software Required Skills: Python (including libraries such as pandas, scikit-learn, TensorFlow, PyTorch) proficiency in developing and deploying models R (optional, but preferred) Data management tools (SQL, NoSQL databases) Cloud platforms (preferably AWS or Azure) for data storage and ML deployment Jupyter Notebooks or similar interactive development environments Version control tools such as Git Preferred Skills: Big data technologies (Spark, Hadoop) Model deployment tools (MLflow, Docker, Kubernetes) Data visualization tools (Tableau, Power BI) Overall Responsibilities Analyze and interpret large and complex data sets to generate insights for business and technology initiatives. Assist in designing, developing, and implementing AI/ML models and algorithms to solve real-world problems. Collaborate with cross-functional teams including data engineers, software developers, and business analysts to integrate models into production systems. Stay current with emerging trends, research, and best practices in AI/ML/Data Science and apply them to ongoing projects. Document methodologies, modeling approaches, and insights clearly for technical and non-technical stakeholders. Support model validation, testing, and performance monitoring to ensure accuracy and reliability. Contribute to the development of data science workflows and standards within the organization. Performance Outcomes: Accurate and reliable data models that support strategic decision-making. Clear documentation and communication of findings and recommendations. Effective collaboration with technical teams to deploy scalable models. Continuous adoption of best practices in AI/ML and data management. Technical Skills (By Category) Programming Languages: Essential: Python (best practices in ML development), SQL Preferred: R, Java (for integration purposes) Databases/Data Management: SQL databases, NoSQL (MongoDB, Cassandra) Cloud data storage solutions (AWS S3, Azure Blob Storage) Cloud Technologies: AWS (S3, EC2, SageMaker, Lambda) Azure Machine Learning (preferred) Frameworks & Libraries: TensorFlow, PyTorch, scikit-learn, Keras, XGBoost Development Tools & Methodologies: Jupyter Notebooks, Git, CI/CD pipelines Agile and Scrum processes Security Protocols: Best practices in data security and privacy, GDPR compliance Experience 8+ years of professional experience in AI, ML, or Data Science roles. Proven hands-on experience designing and deploying ML models in real-world scenarios. Demonstrated ability to analyze complex data sets and translate findings into business insights. Previous experience working with cloud-based data science solutions is preferred. Strong portfolio showcasing data science projects, models developed, and practical impact. Alternative Pathways: Candidates with extensive research or academic experience in AI/ML can be considered, provided they demonstrate practical application of skills. Day-to-Day Activities Conduct data exploration, cleaning, feature engineering, and model development. Collaborate with data engineers to prepare data pipelines for model training. Build, validate, and refine machine learning models. Present insights, models, and recommendations to technical and business stakeholders. Support deployment of models into production environments. Monitor model performance and iterate to improve effectiveness. Participate in team meetings, project planning, and reviewing progress. Document methodologies and maintain version control of codebase. Qualifications Bachelors degree in Computer Science, Mathematics, Statistics, Data Science, or a related field; Masters or PhD highly desirable. Evidence of relevant coursework, certifications, or professional training in AI/ML. Professional certifications (e.g., AWS Certified Machine Learning Specialty, Microsoft Certified Data Scientist) are a plus. Commitment to ongoing professional development in AI/ML methodologies. Professional Competencies Strong analytical and critical thinking to solve complex problems. Effective communication skills for technical and non-technical audiences. Demonstrated ability to work collaboratively in diverse teams. Aptitude for learning new tools, techniques, and technologies rapidly. Innovation mindset with a focus on applying emerging research. Strong organizational skills to manage multiple projects and priorities.

Project Role : Software Development Lead Project Role Description : Develop and configure software systems either end-to-end or for a specific stage of product lifecycle. Apply knowledge of technologies, applications, methodologies, processes and tools to support a client, project or entity. Must have skills : Oracle Cloud Infrastructure Administration Good to have skills : Oracle Advanced Access ControlsMinimum 12 year(s) of experience is required Educational Qualification : 15 years full time education Summary :As a Software Development Lead, you will engage in the development and configuration of software systems, either managing the entire process or focusing on specific stages of the product lifecycle. Your day will involve applying your extensive knowledge of various technologies, applications, methodologies, and tools to effectively support clients and projects, ensuring that all systems are optimized for performance and functionality. You will also collaborate with team members to address challenges and implement innovative solutions that enhance the overall project outcomes. Roles & Responsibilities:- Expected to be an SME.- Collaborate and manage the team to perform.- Responsible for team decisions.- Engage with multiple teams and contribute on key decisions.- Expected to provide solutions to problems that apply across multiple teams.- Facilitate knowledge sharing and mentoring within the team to enhance overall skill levels.- Monitor project progress and ensure alignment with strategic goals. Professional & Technical Skills: - Must To Have Skills: Proficiency in Oracle Cloud Infrastructure Administration.- Strong understanding of cloud architecture and deployment models.- Experience with infrastructure as code tools such as Terraform or Ansible.- Familiarity with security best practices in cloud environments.- Ability to troubleshoot and optimize cloud-based applications. Additional Information:- The candidate should have minimum 12 years of experience in Oracle Cloud Infrastructure Administration.- This position is based at our Bengaluru office.- A 15 years full time education is required. Qualification 15 years full time education

Role Overview : Hiring an ML Engineer with experience in Cloudera ML to support end-to-end model development, deployment, and monitoring on the CDP platform. Key Responsibilities : Develop and deploy models using CML workspaces Build CI/CD pipelines for ML lifecycle Integrate with governance and monitoring tools Enable secure model serving via REST APIs Required education Bachelor's Degree Preferred education Master's Degree Required technical and professional expertise Skills Required : Experience in Cloudera ML, Spark MLlib, or scikit-learn ML pipeline automation (MLflow, Airflow, or equivalent) Model governance, lineage, and versioning API exposure for real-time inference

Role Summary: The Senior AIML Research and Development Engineer will lead complex AI/ML projects, mentor junior engineers, and work on cutting-edge research to enhance AI technologies. This role requires expertise in machine learning, algorithm design, and model deployment. You can apply directly using the link below: https://jobs.lever.co/welocalize/a888c55f-ec73-4d27-9174-f1c5fa11d2c7 Tasks and Responsibilities: Lead the development of AI/ML models and solutions for advanced applications. Drive research initiatives focused on optimizing algorithms and methodologies. Oversee the design and implementation of scalable AI systems. Perform advanced data analysis to extract valuable insights for model improvement. Mentor junior engineers and guide them in their technical development. Collaborate with other departments to ensure successful AI integration into products. Requirements: Bachelors degree in Computer Science, AI/ML, or related field (Masters/PhD preferred). 6+ years of experience in AI/ML research and development. Expertise in machine learning frameworks and algorithms. Advanced programming skills in Python, C++, and Java. Experience with deploying models in production environments. Leadership and mentorship experience, with excellent communication skills.

AI Ops Senior Architect 12 -17 Years Work Location - Pune/ Bengaluru/Hyderabad/Chennai/ Gurugram Tredence is Data science, engineering, and analytics consulting company that partners with some of the leading global Retail, CPG, Industrial and Telecom companies. We deliver business impact by enabling last mile adoption of insights by uniting our strengths in business analytics, data science and data engineering. Headquartered in the San Francisco Bay Area, we partner with clients in US, Canada, and Europe. Bangalore is our largest Centre of Excellence with skilled analytics and technology teams serving our growing base of Fortune 500 clients. JOB DESCRIPTION At Tredence, you will lead the evolution of Industrializing AI ” solutions for our clients by implementing ML/LLM/GenAI & Agent Ops best practices. You will lead the Architecture , Design & development of large scale ML/LLMOps platforms for our clients. You’ll build and maintain tools for deployment, monitoring, and operations. You’ll be a trusted advisor to our clients in ML/GenAI/Agent Ops space & coach to the ML engineering practitioners to build effective solutions to Industrialize AI solutions THE IDEAL CANDIDATE WILL BE RESPONSIBLE FOR AI Ops Strategy, Innovation, Research and Technical Standards 1. Conduct research and experiment with emerging AI Ops technologies and trends. Create POV’s, POC’s & present Proof of Technology to use latest tools, Technologies & services from Hyper scalers focussed on ML, GenAI & Agent Ops 2. Define and propose new technical standards and best practices for the organization's AI Ops environment. 3. Lead the evaluation and adoption of innovative MLOps solutions to address critical business challenges. 4. Conduct meet ups, attend & present in Industry events, conferences, etc 5. Ideate & develop accelerators to strengthen service offerings of AI Ops practice Solution Design & Architectural Development 6. Lead Design & architecture of scalable model training & deployment pipelines for large-scale deployments 7. Architect & Design large scale ML & GenAI Ops platforms 8. Collaborate with Data science & GenAI practice to define and implement strategies of AI solutions for model explainability and interpretability 9. Mentor and guide senior architects in crafting cutting-edge AI Ops solutions 10. Lead architecture reviews and identify opportunities for significant optimizations and improvements. Documentation and Best Practices 11. Develop and maintain comprehensive documentation of AIOps architectures designs and best practices. 12. Lead the development and delivery of training materials and workshops on AIOps tools and techniques. 13. Actively participate in sharing knowledge and expertise with the MLOps team through internal presentations and code reviews. Qualifications and Skills: 1. Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field with minimum 12 years of experience 2. Proven experience in architecting & developing AIOps solutions – to streamline Machine Learning & GenAI development lifecycle 3. Proven experience as an AI Ops Architect – ML & GenAI in architecting & design of ML & GenAI platforms 4. Hands on experience in Model deployment strategies, Designing ML & GenAI model pipelines to scale in production, Model Observability techniques used to monitor performance of ML & LLM’s 5. Strong coding skills with experience in implementing best coding practices Technical Skills & Expertise Python, PySpark, PyTorch ,Java, Micro Services, API’s LLMOps – Vector DB, RAG, LLM Orchestration tools, LLM Observability, LLM Guardrails, Responsible AI MLOps - MLFlow, ML/DL libraries, Model & Data Drift Detection libraries & techniques Real Time & Batch Streaming Container Orchestration Platforms Cloud platforms – Azure/ AWS/ GCP, Data Platforms – Databricks/ Snowflake Nice to Have: Understanding of Agent Ops Exposure to Databricks platform You can expect to – Work with world’s biggest Retailers, CPG’s, HealthCare, Banking & Manufacturing customers and help them solve some of their most critical problems Create multi-million Dollar business opportunities by leveraging impact mindset, cutting edge solutions and industry best practices. Work in a diverse environment that keeps evolving Hone your entrepreneurial skills as you contribute to growth of the organization

Project Role : Application Lead Project Role Description : Lead the effort to design, build and configure applications, acting as the primary point of contact. Must have skills : SAP FI S/4HANA Accounting Good to have skills : NAMinimum 7.5 year(s) of experience is required Educational Qualification : 15 years full time educationJob Summary :We are seeking a highly skilled and experienced Senior SAP S/4HANA Finance Consultant to lead and deliver end-to-end SAP S/4HANA Finance solutions across on-premise, private cloud, and public cloud environments. However, experience in Public Cloud is plus. The ideal candidate will have deep expertise in financial processes, SAP S/4HANA Finance modules, and deployment strategies, along with strong leadership and client-facing skills.Key Responsibilities:Lead the design, implementation, and optimization of SAP S/4HANA Finance solutions.Collaborate with stakeholders to gather business requirements and translate them into technical solutions.Provide expertise in financial modules such as General Ledger (GL), Accounts Payable (AP), Accounts Receivable (AR) including credit and Collection Managment, Asset Accounting (AA), Treasury/Banking and additionally Controlling (CO) knowledge if have.Manage deployment strategies across on-premise, private cloud, and public cloud environments. However, experience in Public Cloud is plus.Conduct workshops and training sessions for clients and internal teams.Ensure compliance with financial regulations and standards during implementation.Perform system integration, testing, and troubleshooting to ensure seamless operations.Provide thought leadership and guidance on SAP S/4HANA Finance best practices.Mentor junior team members and contribute to knowledge sharing within the organization.Required Skills and Qualifications:10+ years of experience in SAP Finance solutions, with at least 5 years in SAP S/4HANA Finance.Expertise in financial processes and SAP modules (GL, AP, AR, AA, Treasury/Banking & CO).Strong knowledge of deployment models (on-premise, private cloud, public cloud).Proven experience in leading SAP S/4HANA Finance implementations.Excellent problem-solving and analytical skills.Strong communication and client-facing skills.Bachelor's degree in Finance, Accounting, or a related field; MBA or equivalent is preferred.Preferred Skills: Experience with RISE with SAP and SAP Activate methodology.Knowledge of integration with other SAP modules (e.g., MM, SD).Familiarity with advanced technologies like Fiori apps and analytics tools. Qualification 15 years full time education

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by diversity and inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health equity on a global scale. Join us to start Caring. Connecting. Growing together Optum is looking for a Manager Data Engineer with deep subject matter expertise in text processing, who will be part of a team leading the technical development and inventions that allow Optum machine learning products drive positive impact in the healthcare business. Leverage UHG’s big data analytics infrastructure, as well as cloud environments, comprising Hadoop, R, Python, HIVE, SPARK to tackle previously unsolved problems. Your expertise will bring business and industry context to science and technology decisions. Your code, designs and documents are exemplary and are used as references across the organization. A successful candidate is a hands-on AI/ML engineering expert that will tackle intrinsically hard problems, acquiring expertise as needed. The candidate will help decompose complex problems into straightforward solutions. Primary Responsibilities: Help design and develop the next generation of NLP, ML & AI products, and services for healthcare Develop machine learning and deep learning models and systems in domains including, but not limited toNLP, NLU, NLG, SLU and multidimensional time series forecasting among others Ability to optimize GenAI models Manage NLP & ML models lifecycle for a suite of products Run large complex proof-of-concepts for the business Manage prioritization and technology work for building NLP, ML & AI solutions Lead the full end-to-end machine learning development process including data ingestion and preparation, feature engineering, analysis and modeling, model deployment, performance tracking and documentation Establish best practices for end-to-end deep learning and machine learning development cycle to ensure rigor in process and quality in outcome Work with a great deal of autonomy to find solutions to complex problems Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so Qualifications Required Qualifications: Graduate degree in applicable area of expertise or equivalent experience Experience in deploying scalable solutions to complex problems, from defining the problem, implementing the solution, and launching the new product successfully Experience in the health care industry Experience with LLM’s like Langchain, Autogen, MCP, RAG etc. and knowledge on Transformers Architecture Statistics, Machine Learning Models, Model Deployment Proven excellent communication, writing and presentation skills Demonstrated hands on experience in using algorithm libraries / frameworks like PyTorch, TensorFlow Demonstrated hands on experience in working with Big Data technologies such as SPARK Knowledge of Azure or GCP or AWS Worked hands-on on the cloud and have familiarity with cloud-based AI/ML services Preferred Qualification: Experience in the health care industry At UnitedHealth Group, our mission is to help people live healthier lives and make the health system work better for everyone. We believe everyone - of every race, gender, sexuality, age, location and income - deserves the opportunity to live their healthiest life. Today, however, there are still far too many barriers to good health which are disproportionately experienced by people of color, historically marginalized groups and those with lower incomes. We are committed to mitigating our impact on the environment and enabling and delivering equitable care that addresses health disparities and improves health outcomes - an enterprise priority reflected in our mission.

ML Engineer (ML Ops)Chennai / Bangalore / Hyderabad Curious about the role? What your typical day would look like? We are looking for a Machine Learning Engineer/Sr MLE who will work on a broad range of cutting-edge data analytics and machine learning problems across a variety of industries. More specifically, you will Engage with clients to understand their business context. Translate business problems and technical constraints into technical requirements for the desired analytics solution. Collaborate with a team of data scientists and engineers to embed AI and analytics into the business decision processes. What do we expect? 6+ years of experience with at least 4+ years of relevant MLOps experience . Proficient in a structured Python (Mandate) Proficient in any one of cloud technologies is mandatory ( AWS/ Azure/ GCP) Proficient in Azure Databricks Follows good software engineering practices and has an interest in building reliable and robust software. Good understanding of DS concepts and DS model lifecycle. Working knowledge of Linux or Unix environments ideally in a cloud environment. Working knowledge of Spark/ PySpark is desirable. Model deployment / model monitoring experience is desirable. CI/CD pipeline creation is good to have. Excellent written and verbal communication skills. B.Tech from Tier-1 college / M.S or M. Tech is preferred. You are important to us, lets stay connected! Every individual comes with a different set of skills and qualities so even if you don’t tick all the boxes for the role today, we urge you to apply as there might be a suitable/unique role for you tomorrow.We are an equal-opportunity employer. Our diverse and inclusive culture and values guide us to listen, trust, respect, and encourage people to grow the way they desire. Note: The designation will be commensurate with expertise and experience. Compensation packages are among the best in the industry.Additional Benefits: Health insurance (self & family), virtual wellness platform, and knowledge communities.

Mandatory Skills- NLP,GenAI,Machine Learning,Deployment,LLM,MLops Design, develop, and deploy NLP & Generative AI solutions, leveraging Large Language Models (LLMs), fine-tuning techniques, and AI-powered automation. Lead research and implementation of advanced NLP techniques, including transformers, embeddings, retrieval-augmented generation (RAG), and multi-modal models. Architect scalable NLP pipelines for text processing, entity recognition, summarization, question answering, and conversational AI. Develop and optimize LLM-powered chatbots, virtual assistants, and AI agents, ensuring efficiency, accuracy, and contextual awareness. Implement Agentic AI systems, enabling autonomous workflows powered by LLMs and task orchestration frameworks. Ensure LLM observability and guardrails, enhancing model monitoring, safety, fairness, and compliance in production environments. Optimize inference pipelines, leveraging quantization, model distillation, and retrieval-enhanced generation to improve performance and cost efficiency. Lead MLOps initiatives, including CI/CD pipelines, containerization (Docker, Kubernetes), and cloud deployments (AWS, GCP, Azure). Collaborate with cross-functional teams to integrate NLP & GenAI solutions into enterprise applications, ensuring robust API development and scalable microservices architecture. Mentor junior engineers, drive best practices in NLP/AI model development, and contribute to AI governance in regulated industries like pharma/life sciences. Key Qualifications: 7-9 years of experience in NLP, AI/ML, or data science, with a proven track record of delivering production-grade NLP & GenAI solutions. Deep expertise in LLMs, transformer architectures (BERT, GPT, T5, LLaMA, Mistral, etc.), and fine-tuning techniques. Strong knowledge of NLP pipelines, including text preprocessing, tokenization, embeddings, and named entity recognition (NER). Experience with retrieval-augmented generation (RAG), vector databases (FAISS, Pinecone, Chroma), and prompt engineering. Hands-on experience with Agentic AI systems, LLM observability tools, and AI safety guardrails. Proficiency in Python and backend development (Django/Flask preferred), with strong API and microservices expertise. Familiarity with MLOps, cloud platforms (AWS, GCP, Azure), and scalable model deployment strategies. Prior experience in life sciences, pharma, or other regulated industries is a plus. A problem-solving mindset with the ability to work independently, drive innovation, and mentor junior engineers.

Mandatory Skills- NLP,GenAI,Machine Learning,Deployment,LLM,MLops Design, develop, and deploy NLP & Generative AI solutions, leveraging Large Language Models (LLMs), fine-tuning techniques, and AI-powered automation. Lead research and implementation of advanced NLP techniques, including transformers, embeddings, retrieval-augmented generation (RAG), and multi-modal models. Architect scalable NLP pipelines for text processing, entity recognition, summarization, question answering, and conversational AI. Develop and optimize LLM-powered chatbots, virtual assistants, and AI agents, ensuring efficiency, accuracy, and contextual awareness. Implement Agentic AI systems, enabling autonomous workflows powered by LLMs and task orchestration frameworks. Ensure LLM observability and guardrails, enhancing model monitoring, safety, fairness, and compliance in production environments. Optimize inference pipelines, leveraging quantization, model distillation, and retrieval-enhanced generation to improve performance and cost efficiency. Lead MLOps initiatives, including CI/CD pipelines, containerization (Docker, Kubernetes), and cloud deployments (AWS, GCP, Azure). Collaborate with cross-functional teams to integrate NLP & GenAI solutions into enterprise applications, ensuring robust API development and scalable microservices architecture. Mentor junior engineers, drive best practices in NLP/AI model development, and contribute to AI governance in regulated industries like pharma/life sciences. Key Qualifications: 7-9 years of experience in NLP, AI/ML, or data science, with a proven track record of delivering production-grade NLP & GenAI solutions. Deep expertise in LLMs, transformer architectures (BERT, GPT, T5, LLaMA, Mistral, etc.), and fine-tuning techniques. Strong knowledge of NLP pipelines, including text preprocessing, tokenization, embeddings, and named entity recognition (NER). Experience with retrieval-augmented generation (RAG), vector databases (FAISS, Pinecone, Chroma), and prompt engineering. Hands-on experience with Agentic AI systems, LLM observability tools, and AI safety guardrails. Proficiency in Python and backend development (Django/Flask preferred), with strong API and microservices expertise. Familiarity with MLOps, cloud platforms (AWS, GCP, Azure), and scalable model deployment strategies. Prior experience in life sciences, pharma, or other regulated industries is a plus. A problem-solving mindset with the ability to work independently, drive innovation, and mentor junior engineers.

Mandatory Skills- NLP,GenAI,Machine Learning,Deployment,LLM,MLops Design, develop, and deploy NLP & Generative AI solutions, leveraging Large Language Models (LLMs), fine-tuning techniques, and AI-powered automation. Lead research and implementation of advanced NLP techniques, including transformers, embeddings, retrieval-augmented generation (RAG), and multi-modal models. Architect scalable NLP pipelines for text processing, entity recognition, summarization, question answering, and conversational AI. Develop and optimize LLM-powered chatbots, virtual assistants, and AI agents, ensuring efficiency, accuracy, and contextual awareness. Implement Agentic AI systems, enabling autonomous workflows powered by LLMs and task orchestration frameworks. Ensure LLM observability and guardrails, enhancing model monitoring, safety, fairness, and compliance in production environments. Optimize inference pipelines, leveraging quantization, model distillation, and retrieval-enhanced generation to improve performance and cost efficiency. Lead MLOps initiatives, including CI/CD pipelines, containerization (Docker, Kubernetes), and cloud deployments (AWS, GCP, Azure). Collaborate with cross-functional teams to integrate NLP & GenAI solutions into enterprise applications, ensuring robust API development and scalable microservices architecture. Mentor junior engineers, drive best practices in NLP/AI model development, and contribute to AI governance in regulated industries like pharma/life sciences. Key Qualifications: 7-9 years of experience in NLP, AI/ML, or data science, with a proven track record of delivering production-grade NLP & GenAI solutions. Deep expertise in LLMs, transformer architectures (BERT, GPT, T5, LLaMA, Mistral, etc.), and fine-tuning techniques. Strong knowledge of NLP pipelines, including text preprocessing, tokenization, embeddings, and named entity recognition (NER). Experience with retrieval-augmented generation (RAG), vector databases (FAISS, Pinecone, Chroma), and prompt engineering. Hands-on experience with Agentic AI systems, LLM observability tools, and AI safety guardrails. Proficiency in Python and backend development (Django/Flask preferred), with strong API and microservices expertise. Familiarity with MLOps, cloud platforms (AWS, GCP, Azure), and scalable model deployment strategies. Prior experience in life sciences, pharma, or other regulated industries is a plus. A problem-solving mindset with the ability to work independently, drive innovation, and mentor junior engineers.

Overview We are PepsiCo We believe that acting ethically and responsibly is not only the right thing to do, but also the right thing to do for our business. At PepsiCo, we aim to deliver top-tier financial performance over the long term by integrating sustainability into our business strategy, leaving a positive imprint on society and the environment. We call this Winning with Pep+ Positive . For more information on PepsiCo and the opportunities it holds, visitwww.pepsico.com. PepsiCo Data Analytics & AI Overview With data deeply embedded in our DNA, PepsiCo Data, Analytics and AI (DA&AI) transforms data into consumer delight. We build and organize business-ready data that allows PepsiCos leaders to solve their problems with the highest degree of confidence. Our platform of data products and services ensures data is activated at scale. This enables new revenue streams, deeper partner relationships, new consumer experiences, and innovation across the enterprise. The Data Science Pillar in DA&AI will be the organization where Data Scientist and ML Engineers report to in the broader D+A Organization. Also DS will lead, facilitate and collaborate on the larger DS community in PepsiCo. DS will provide the talent for the development and support of DS component and its life cycle within DA&AI Products. And will support pre-engagement activities as requested and validated by the prioritization framework of DA&AI. Data Scientist-Gurugram and Hyderabad The role will work in developing Machine Learning (ML) and Artificial Intelligence (AI) projects. Specific scope of this role is to develop ML solution in support of ML/AI projects using big analytics toolsets in a CI/CD environment. Analytics toolsets may include DS tools/Spark/Databricks, and other technologies offered by Microsoft Azure or open-source toolsets. This role will also help automate the end-to-end cycle with Machine Learning Services and Pipelines. Responsibilities Delivery of key Advanced Analytics/Data Science projects within time and budget, particularly around DevOps/MLOps and Machine Learning models in scope Collaborate with data engineers and ML engineers to understand data and models and leverage various advanced analytics capabilities Ensure on time and on budget delivery which satisfies project requirements, while adhering to enterprise architecture standards Use big data technologies to help process data and build scaled data pipelines (batch to real time) Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure/AWS/GCP Pipelines. Setup cloud alerts, monitors, dashboards, and logging and troubleshoot machine learning infrastructure Automate ML models deployments Qualifications Minimum 3years of hands-on work experience in data science / Machine learning Minimum 3year of SQL experience Experience in DevOps and Machine Learning (ML) with hands-on experience with one or more cloud service providers BE/BS in Computer Science, Math, Physics, or other technical fields. Data Science Hands on experience and strong knowledge of building machine learning models supervised and unsupervised models Programming Skills Hands-on experience in statistical programming languages like Python and database query languages like SQL Statistics Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators Any Cloud Experience in Databricks and ADF is desirable Familiarity with Spark, Hive, Pigis an added advantage Model deployment experience will be a plus Experience with version control systems like GitHub and CI/CD tools Experience in Exploratory data Analysis Knowledge of ML Ops / DevOps and deploying ML models is required Experience using MLFlow, Kubeflow etc. will be preferred Experience executing and contributing to ML OPS automation infrastructure is good to have Exceptional analytical and problem-solving skills

Overview We are PepsiCo We believe that acting ethically and responsibly is not only the right thing to do, but also the right thing to do for our business. At PepsiCo, we aim to deliver top-tier financial performance over the long term by integrating sustainability into our business strategy, leaving a positive imprint on society and the environment. We call this Winning with Pep+ Positive . For more information on PepsiCo and the opportunities it holds, visitwww.pepsico.com. PepsiCo Data Analytics & AI Overview With data deeply embedded in our DNA, PepsiCo Data, Analytics and AI (DA&AI) transforms data into consumer delight. We build and organize business-ready data that allows PepsiCos leaders to solve their problems with the highest degree of confidence. Our platform of data products and services ensures data is activated at scale. This enables new revenue streams, deeper partner relationships, new consumer experiences, and innovation across the enterprise. The Data Science Pillar in DA&AI will be the organization where Data Scientist and ML Engineers report to in the broader D+A Organization. Also DS will lead, facilitate and collaborate on the larger DS community in PepsiCo. DS will provide the talent for the development and support of DS component and its life cycle within DA&AI Products. And will support pre-engagement activities as requested and validated by the prioritization framework of DA&AI. Data Scientist-Gurugram and Hyderabad The role will work in developing Machine Learning (ML) and Artificial Intelligence (AI) projects. Specific scope of this role is to develop ML solution in support of ML/AI projects using big analytics toolsets in a CI/CD environment. Analytics toolsets may include DS tools/Spark/Databricks, and other technologies offered by Microsoft Azure or open-source toolsets. This role will also help automate the end-to-end cycle with Machine Learning Services and Pipelines. Responsibilities Delivery of key Advanced Analytics/Data Science projects within time and budget, particularly around DevOps/MLOps and Machine Learning models in scope Collaborate with data engineers and ML engineers to understand data and models and leverage various advanced analytics capabilities Ensure on time and on budget delivery which satisfies project requirements, while adhering to enterprise architecture standards Use big data technologies to help process data and build scaled data pipelines (batch to real time) Automate the end-to-end ML lifecycle with Azure Machine Learning and Azure/AWS/GCP Pipelines. Setup cloud alerts, monitors, dashboards, and logging and troubleshoot machine learning infrastructure Automate ML models deployments Qualifications Minimum 3years of hands-on work experience in data science / Machine learning Minimum 3year of SQL experience Experience in DevOps and Machine Learning (ML) with hands-on experience with one or more cloud service providers. BE/BS in Computer Science, Math, Physics, or other technical fields. Data Science Hands on experience and strong knowledge of building machine learning models supervised and unsupervised models Programming Skills Hands-on experience in statistical programming languages like Python and database query languages like SQL Statistics Good applied statistical skills, including knowledge of statistical tests, distributions, regression, maximum likelihood estimators Any Cloud Experience in Databricks and ADF is desirable Familiarity with Spark, Hive, Pigis an added advantage Model deployment experience will be a plus Experience with version control systems like GitHub and CI/CD tools Experience in Exploratory data Analysis Knowledge of ML Ops / DevOps and deploying ML models is required Experience using MLFlow, Kubeflow etc. will be preferred Experience executing and contributing to ML OPS automation infrastructure is good to have Exceptional analytical and problem-solving skills

Role : Architect Software Engineering Experience : 10+ years Job Location : Chennai About OJ Commerce: OJ Commerce (OJC), a rapidly expanding and profitable online retailer, is headquartered in Florida, USA, with a fully functional office in Chennai, India, We deliver exceptional value to our customers by harnessing cutting-edge technology, fostering innovation, and establishing strategic brand partnerships to enable a seamless, enjoyable shopping experience featuring high-quality products at unbeatable prices Our advanced, data-driven system streamlines operations with minimal human intervention, Our extensive product portfolio encompasses over a million SKUs and more than 2,500 brands across eight primary categories With a robust presence on major platforms such as Amazon, Walmart, Wayfair, Home Depot, and eBay, we directly serve consumers in the United States, As we continue to forge new partner relationships, our flagship website, ojcommerce , has rapidly emerged as a topperforming e-commerce channel, catering to millions of customers annually, Responsibilities: Lead the design and implementation of scalable, secure, and high-performing solutions using Dot net and cloud technologies, Create comprehensive HLD and LLD documents that clearly outline the system architecture, components, and integration points, Develop and implement cloud strategies, with a preference for GCP, including migration, deployment, and management of cloud-based applications, Provide technical direction and mentorship to development teams, ensuring best practices and architectural standards are followed, Work closely with business stakeholders, project managers, and development teams to understand requirements, define solutions, and ensure successful delivery, Identify and resolve performance bottlenecks, ensuring systems are efficient and scalable, Ensure all solutions adhere to security best practices and compliance requirements, Skill Sets: Proficiency in Dot net framework and associated technologies, Solid understanding of cloud services, architectures, and deployment models, Experience with system design patterns, RESTful APIs, and containerization (Docker, Kubernetes), Familiarity with DevOps practices and tools (CI/CD, Jenkins, Git, etc ), Knowledge of database systems (SQL, NoSQL) and data modelling, Expertise in refactoring, performance tuning, and coding design patterns, Knowledge of version control systems, such as GIT, Knowledge in unit testing, Excellent problem-solving skills and attention to detail, Strong communication skills and the ability to work well in a team environment, Candidates with experience in ecommerce / online retail preferred, What We Offer Competitive salary Medical Benefits/Accident Cover Flexi Office Working Hours Fast paced start up

Role & responsibilities Develop, implement & fine-tune deep learning AI models for wide range of Computer Vision application including object classification, object detection, segmentation, OCR & NLP Perform data annotation using automation script & preprocessing to prepare high-quality datasets for training our in-house deep learning models. Training of AI models with model inference and evaluate model accuracy & performance metrics using frameworks like PyTorch, Tensorflow Collaborate with software engineers to deploy models in production systems, ensuring scalability, reliability and efficiency. Develop APIs using FastAPI or similar frameworks to enable seamless integration and interaction with deployed models Experience in Deep Learning, Computer Vision, NLP with a focus on object classification, detection, segmentation, OCR & text processing. Pytho , C++, Understanding of deep learning frameworks such as Tensorflow, PyTorc,h or Keras Experience with AI model training,hyperparameters tunin,g and evaluation of large-scale datasets. Data Annotation familiarity & preprocessing techniques to prepare datasets for deep learning models. Version control systems like Git Problem-solving skills, communication skills, collaboration skills, team player AI Model deployment & serving, including the use of APIs and frameworks like FastAPI

4+ years of Anaplan model building experience (Anaplan L3 or Solution Architect Certification) Strong technical background in consulting, finance, or software implementation. Expertise in building, troubleshooting complex multidimensional models. Required Candidate profile Experience in stakeholder management. Knowledge of data architecture and cross-platform integration. Mulesoft integration experience is a plus.

Skills : Appian development, Process Models, SAIL Interfaces, Web APIs Continuous Integration and Deployment model, Test Driven Development, Behavioral Driven Development and BFSI Domain knowledge, SOAP and Restful webservices Required Candidate profile Notice Period: immediate Education: Full time graduate Experience: 4 to 11 years, 3+ years in Appian L1/L2/L3 certification is mandatory atleast L1 certified minimum

Management Level :09 - Senior Consultant Location :Gurgaon/Bangalore/Mumbai Must have skills :Python Programming, Machine Learning, Statistical Analysis, Data Science, Generative AI (GPT, DALLE, GANs), Cloud (AWS/Azure/GCP), Model Deployment, API Development, Docker/Kubernetes Good to have skills :PyTorch, TensorFlow, Large-Scale Data Processing, Cloud Integrations, NLP, Computer Vision, Security Best Practices, Performance Optimization, Open-Source Contributions Job Summary : Accenture AI is seeking a talented and motivated Generative AI Data Scientist Consultant to join our Banking team. The ideal candidate will leverage their expertise in Generative AI models, Machine Learning, Data Analysis, and Cloud Platforms to develop innovative solutions and enhance AI capabilities. This role will involve collaborating with cross-functional teams to design, develop, and implement generative AI solutions that meet our business needs. Roles & Responsibilities: Collaboration :Work closely with product managers, engineers, and other stakeholders to understand requirements and deliver impactful AI solutions. Model Deployment :Containerize and deploy generative AI models using tools like Docker, Kubernetes, and cloud platforms (e.g., AWS, Azure, Google Cloud). API Development :Design, develop, and deploy robust APIs to expose generative AI models (e.g., GPT, DALLE, Stable Diffusion, GANs) for integration into client systems and applications. Client Collaboration :Work closely with clients to understand their technical requirements and deliver tailored API solutions that meet their business needs. Generative AI Model Integration :Integrate generative AI models into existing workflows, applications, and platforms via APIs. Performance Optimization :Optimize API performance for latency, scalability, and reliability, ensuring seamless user experiences. Data Pipeline Development :Build and maintain data pipelines to support real-time or batch processing for generative AI models. Security and Compliance :Implement security best practices and ensure APIs comply with data privacy and regulatory standards. Documentation :Create comprehensive API documentation, including usage examples, SDKs, and troubleshooting guides. Testing and Monitoring :Develop automated testing frameworks and monitoring systems to ensure API reliability and performance. Stay Updated :Keep up with the latest advancements in generative AI, API development, and cloud technologies to bring innovative solutions to clients. Professional & Technical Skills: 3+ years of experience in Data Science and Machine Learning with a focus on Generative AI models (LLMs). Strong proficiency in Python programming and experience with machine learning frameworks (e.g., TensorFlow, PyTorch ). Expertise in API Development and deployment, with hands-on experience with Docker , Kubernetes , and cloud platforms (AWS, GCP, Azure). Ability to build and optimize data pipelines for generative AI models. Knowledge of security best practices and ensuring compliance with regulatory standards. Strong problem-solving, analytical, and creative thinking skills to design and implement innovative solutions. Excellent communication skills, capable of conveying complex technical concepts to diverse audiences. Additional Information: Experience with large-scale data processing and expertise in NLP or computer vision . Contributions to open-source AI/ML projects . Experience with cloud integrations , big data technologies , and performance optimization . Familiarity with security standards and regulatory compliance in AI solutions. Qualifications Experience :Minimum 3+ years of experience in Data Science, Machine Learning, or related fields with a focus on Generative AI models (LLMs) and API development . Educational Qualification :Bachelors or Masters in Computer Science, Data Science, Statistics, Engineering, or related Analytics discipline from a premier institute.

Management Level :07 - Manager Location :Gurgaon/Bangalore/Mumbai Must have skills :Python Programming, Machine Learning, Generative AI (GPT, DALLE, GANs), Model Deployment, Cloud (AWS/Azure/GCP), Statistical Analysis, Data Science, API Development, Docker/Kubernetes Good to have skills :PyTorch, TensorFlow, NLP, Computer Vision, Performance Optimization, Ethical AI Practices, Cloud Integrations, Open-Source Contributions Job Summary : Accenture AI is seeking a talented and motivated Generative AI Data Scientist Lead to join our Banking team. The ideal candidate will leverage their expertise in Generative AI models, Machine Learning, Data Analysis and Cloud Platforms to drive and lead the development & delivery of the innovative solutions and enhance our AI capabilities. This role will involve collaborating with cross-functional teams to design, develop, and implement generative AI solutions that meet our business needs. Roles & Responsibilities: Independent Project Leadership :Take ownership of end-to-end project delivery, from problem definition to solution deployment, with minimal supervision. Pod Leadership :Lead and mentor a small development pod (team) of data scientists and engineers, ensuring collaboration, innovation, and timely delivery of high-quality solutions. Model Development :Design, develop, and deploy state-of-the-art generative AI models (e.g., GPT, DALLE, Stable Diffusion, GANs) for applications such as text generation, image synthesis, content creation, and more. Strategy Development :Provide strategic recommendations to clients on how to integrate generative AI into their workflows and business processes. Ethical AI Practices :Ensure responsible use of generative AI by addressing biases, ethical concerns, and compliance with regulations. Performance Evaluation :Develop evaluation metrics and methodologies to assess the performance of generative models and ensure they meet quality standards. Collaboration :Work closely with product managers, engineers, and other stakeholders to understand requirements and deliver impactful AI solutions. Research and Innovation :Stay up-to-date with the latest advancements in generative AI and machine learning, applying that knowledge to improve existing models and techniques. Mentorship :Guide and mentor junior team members, fostering a culture of innovation and continuous learning. Professional & Technical Skills: 6+ years of experience in Data Science , Machine Learning , or related fields with a focus on Generative AI models (LLMs) . Expert proficiency in Python programming and experience with machine learning frameworks (e.g., TensorFlow , PyTorch ). Hands-on experience with cloud platforms (AWS, GCP, Azure), including deployment and solution building . Ability to work with Docker , Kubernetes , and API Development to deploy generative AI models. Strong analytical, problem-solving , and critical thinking skills. Expertise in ethical AI practices and understanding of the biases and regulations surrounding Generative AI . Excellent verbal and written communication skills, with the ability to clearly convey complex concepts to diverse audiences. Additional Information: Experience with large-scale data processing and cloud computing platforms. Familiarity with NLP , computer vision , and other AI applications. Publications or contributions to open-source AI/ML projects . Experience in cloud integrations and performance optimization . Strong leadership skills with experience in guiding teams and managing AI projects. Qualifications Experience :Minimum 6+ years of experience in Data Science, Machine Learning, or a related field with a focus on Generative AI models (LLMs) and leadership in AI solutions. Educational Qualification :Masters or Ph.D. in Computer Science , Data Science , Statistics , or related field.

Management Level :07 - Manager Location :Gurgaon/Bangalore/Mumbai Must have skills :Python Programming, Machine Learning, Generative AI (GPT, DALLE, GANs), Model Deployment, Cloud (AWS/Azure/GCP), Statistical Analysis, Data Science, API Development, Docker/Kubernetes Good to have skills :PyTorch, TensorFlow, NLP, Computer Vision, Performance Optimization, Ethical AI Practices, Cloud Integrations, Open-Source Contributions Job Summary : Accenture AI is seeking a talented and motivated Generative AI Data Scientist Lead to join our Banking team. The ideal candidate will leverage their expertise in Generative AI models, Machine Learning, Data Analysis and Cloud Platforms to drive and lead the development & delivery of the innovative solutions and enhance our AI capabilities. This role will involve collaborating with cross-functional teams to design, develop, and implement generative AI solutions that meet our business needs. Roles & Responsibilities: Independent Project Leadership :Take ownership of end-to-end project delivery, from problem definition to solution deployment, with minimal supervision. Pod Leadership :Lead and mentor a small development pod (team) of data scientists and engineers, ensuring collaboration, innovation, and timely delivery of high-quality solutions. Model Development :Design, develop, and deploy state-of-the-art generative AI models (e.g., GPT, DALLE, Stable Diffusion, GANs) for applications such as text generation, image synthesis, content creation, and more. Strategy Development :Provide strategic recommendations to clients on how to integrate generative AI into their workflows and business processes. Ethical AI Practices :Ensure responsible use of generative AI by addressing biases, ethical concerns, and compliance with regulations. Performance Evaluation :Develop evaluation metrics and methodologies to assess the performance of generative models and ensure they meet quality standards. Collaboration :Work closely with product managers, engineers, and other stakeholders to understand requirements and deliver impactful AI solutions. Research and Innovation :Stay up-to-date with the latest advancements in generative AI and machine learning, applying that knowledge to improve existing models and techniques. Mentorship :Guide and mentor junior team members, fostering a culture of innovation and continuous learning. Professional & Technical Skills: 6+ years of experience in Data Science , Machine Learning , or related fields with a focus on Generative AI models (LLMs) . Expert proficiency in Python programming and experience with machine learning frameworks (e.g., TensorFlow , PyTorch ). Hands-on experience with cloud platforms (AWS, GCP, Azure), including deployment and solution building . Ability to work with Docker , Kubernetes , and API Development to deploy generative AI models. Strong analytical, problem-solving , and critical thinking skills. Expertise in ethical AI practices and understanding of the biases and regulations surrounding Generative AI . Excellent verbal and written communication skills, with the ability to clearly convey complex concepts to diverse audiences. Additional Information: Experience with large-scale data processing and cloud computing platforms. Familiarity with NLP , computer vision , and other AI applications. Publications or contributions to open-source AI/ML projects . Experience in cloud integrations and performance optimization . Strong leadership skills with experience in guiding teams and managing AI projects. Qualifications Experience :Minimum 6+ years of experience in Data Science, Machine Learning, or a related field with a focus on Generative AI models (LLMs) and leadership in AI solutions. Educational Qualification :Masters or Ph.D. in Computer Science , Data Science , Statistics , or related field.

We are looking for a Machine Learning Engineer with expertise in MLOps to develop, deploy, and maintain scalable AI/ML pipelines . This role requires a strong background in machine learning, model deployment, cloud platforms, and automation to ensure seamless integration of AI models into production systems. You will work closely with data scientists, engineers, and DevOps teams to optimize model performance and reliability. Key Responsibilities Model Development & Deployment Design, build, and deploy scalable and efficient ML models for production use. Implement CI/CD pipelines for ML workflows using GitHub Actions, Jenkins, or GitLab CI/CD . Optimize model inference using ONNX, TensorRT, or TorchScript for faster deployment. Work with MLOps tools like MLflow, Kubeflow, TFX, and SageMaker . MLOps & Model Monitoring Develop and maintain end-to-end ML pipelines , including data ingestion, preprocessing, training, and deployment. Implement model versioning, monitoring, and retraining strategies . Set up automated model performance tracking and real-time anomaly detection using Prometheus, Grafana, or Weights & Biases. Cloud & Infrastructure Deploy models on AWS, GCP, or Azure using services like SageMaker, Vertex AI, or Azure ML . Work with containerization (Docker, Kubernetes) for model serving. Manage serverless AI deployments using Lambda, Cloud Run, or Azure Functions . Collaboration & Best Practices Work with data scientists, software engineers, and DevOps teams to optimize model integration. Establish MLOps best practices , including feature stores, automated testing, and data versioning . Ensure compliance with AI governance, model explainability, and security best practices . Qualifications & Experience Education: Bachelor's/Master's in Computer Science, AI/ML, Data Engineering, or a related field . Experience: 5-8 years in ML engineering, model deployment, and MLOps . Technical Expertise: Strong Python skills ( FastAPI, Flask, PyTorch, TensorFlow, Scikit-learn ). Experience with Kubernetes, Docker, Terraform, and cloud-based AI services . Knowledge of vector databases (FAISS, Pinecone), data pipelines (Airflow, Prefect), and streaming (Kafka, Spark Streaming) . Soft Skills: Strong problem-solving and debugging skills. Ability to work in cross-functional teams and handle production ML challenges. Strong communication and documentation abilities. Nice-to-Have: Experience with Generative AI and LLMOps (e.g., Hugging Face, LangChain, LlamaIndex). Background in edge AI deployments or real-time AI applications . Contributions to open-source AI/ML projects . Required Skills MLops, Model Deployment, Model Monitoring, Model Fine tuning, Sagemaker, Vertex

Job Title AI/ML Responsibilities Data collection, profiling, EDA & data preparation AI Model development, tuning & validation Visualization tools (e.g., PowerBI, tableau) Present findings to business users & project management teams Propose ML based solution approaches and estimates for new use cases Contribute to AI based modules in Infosys solutions development Explore new advances in AI continuously and execute PoCs Mentoringguide other team members ML algorithms AI domainsNLP, speech, computer vision Supervised, Unsupervised, Reinforcement learning Tools for data analysis, auto ML, model deployment and scaling Knowledge of datasets ProgrammingPython Databases Knowledge of cloud platforms Azure/AWS/GCP. Preferred Skills: Technology->Artificial Intelligence->Artificial Intelligence - ALL Educational Requirements Bachelor of Engineering Service Line Quality * Location of posting is subject to business requirements

Short Description: Strategy and Enterprise Analytics Team provides insights and decision support tools to a broad range of business partners in sustainability and Eelectric vehicles. We drive to deliver the most value to Ford through critical thinking, AI/ML, big data analytics and optimization techniques on Google Cloud Platform (GCP). We are looking for a manager / product owner to lead a multi-skill team of data scientists, data engineers and software engineers in all phases of ongoing and future analytics services and product development, including problem formulation, data identification, model development, validation, and product launch. Description: Strategy and Enterprise Analytics Team provides insights and decision support tools to a broad range of business partners in sustainability and Eelectric vehicles. We drive to deliver the most value to Ford through critical thinking, AI/ML, big data analytics and optimization techniques on Google Cloud Platform (GCP). The team leverages advanced analytics and domain knowledge to develop analytics models, transformational decision-support tools, and services to: Increase profit while meeting North American regulatory compliance. Support strategic initiatives for electrification, especially in public charging, home power management, and energy services. Design and build solutions that support holistic decision making at enterprise level (OGC) We are looking for a manager / product owner to lead a multi-skill team of data scientists, data engineers and software engineers in all phases of ongoing and future analytics services and product development, including problem formulation, data identification, model development, validation, and product launch. The candidate should have great independence, exceptional collaboration and leadership skills, and self-discipline to guide original applied research and choose appropriate methodologies to solve related problems. We are especially excited about candidates with supervisory experience, passion for hands-on work, strong technical skills and growth mindset who demonstrate a passion for developing talents and applying state-of-the-art solutions to novel and challenging problems. Our team utilizes a diverse set of tools and methodologies from different technical fields including Machine Learning, Statistical Analysis, Simulation, Big Data platforms and more. We understand that you cannot be an expert in everything, and the set of techniques and technologies we are using today may change over time. Given the required qualifications, we are looking for candidates who are lifelong learners, driven, and curious. Responsibilities: Lead a group of data scientists, data engineers, and software engineers, to solve exciting and meaningful problems. Translate business needs into analytical problems, work hands-on along with the team, judge among candidate ML models, contribute towards best practices in model development, conduct code reviews, research state-of-the art techniques and apply them for the team and business needs. Develop and apply analytical solutions to address real-world automotive and Quality challenges. Initiate and manage cross-functional projects, building relationships with business partners, and influencing decision makers. Ability to work well under limited supervision and use good judgment to know when to update and seek guidance from leadership. Communicate and present insights to business customers and executives. Collaborate internally and externally to identify new and novel data sources and explore their potential use in developing actionable business results. Explore emerging technologies and analytic solutions for use in quantitative model development. Develop and sustain a highly performing team. Ability to collaborate, negotiate, and work effectively with coworkers at all levels. Works with Product Line Owner on creating demand and aligning the requirements to the Business demands. Qualifications: Masters degree in engineering, Data Science, Computer Science, Statistics, Industrial Engineering, or other data-related fields 5+ years of domain experience in delivering analytics solutions in any of these areas (Sustainability / Regulatory, Electrification, Legal, Cycle Planning) 10+ years of experience in analytics domain with 3+ years of supervisory experience Familiarity with SQL, Spark, Hive, and other big data technologies Familiarity with Google Cloud Platform, Python, Spark, Dataflow, BigQuery, GitHub, Qlik Sense, CPLEX, Mach1ML, Power BI Demonstrated performance in working on developing analytical models and deploying them in GCP. Strong drive for results, sense of urgency, and attention to detail Strong verbal and written communication skills with the ability to present to cross functional levels of management. Ability to work in a fast-paced environment with global resources under short response times and changing business needs. Familiarity with NLP, Deep Learning, neural network architectures including CNNs, RNNs, Embeddings, Transfer Learning, and Transformers. BEV experience a plus Deep understanding of Agile & PDO processes Software delivery experience a plus

Job Title :Data Science Engineer. Location :Remote (India, Ahmedabad Preferred). Shift :UK Shift. We are looking for a Data Science Engineer with 6+ years of experience in developing, deploying, and scaling machine learning models in production environments. This hybrid role combines expertise in data science and machine learning engineering to deliver impactful data-driven solutions at scale. Key Responsibilities. Design and develop machine learning models for various business problems. Implement data preprocessing, feature engineering, and model training in Python and Databricks. Work on model deployment, monitoring, and scaling in cloud or on-premise environments. Develop and optimize data pipelines to support model building and execution. Collaborate with cross-functional teams to ensure model alignment with business needs. Ensure the scalability, performance, and reliability of deployed models. Implement MLOps practices for automated testing, continuous integration, and model versioning. Monitor model performance in production and refine models as needed. Requirements. 6+ years of experience in Data Science, Machine Learning, and Model Deployment. Strong expertise in Python, Databricks, and machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch). Experience with data pipeline development and cloud platforms. Knowledge of MLOps practices for scalable and automated model deployment. Strong SQL skills and experience working with large datasets. Familiarity with version control systems (e.g., Git) and containerization (e.g., Docker). Ability to work in a UK shift and collaborate with global teams. Job Type:4 years (Required). Databricks:3 years (Required). Work Location:Remote. (ref:hirist.tech). Show more Show less