Sr Data Scientist

Role Overview: You will be working as a Sr. Data Scientist at LexisNexis Legal & Professional, focusing on complex projects involving AI, GenAI, RAG, and Agentic Solution for business delivery projects. Your role will involve designing, developing, and deploying machine learning and GenAI solutions in production environments, optimizing Retrieval-Augmented Generation (RAG) pipelines, fine-tuning and evaluating Large Language Models (LLMs), developing prompt engineering strategies, and implementing scalable ML pipelines using Python. You will also collaborate with Engineering, Product, and Subject Matter Experts (SMEs) to deliver AI-driven features, monitor model performance in production, conduct experimentation and performance benchmarking, and contribute to the architecture design for AI-powered systems. Key Responsibilities: - Design, develop, and deploy machine learning and GenAI solutions in production environments - Build and optimize Retrieval-Augmented Generation (RAG) pipelines - Fine-tune and evaluate Large Language Models (LLMs) - Develop prompt engineering strategies and evaluation frameworks - Implement scalable ML pipelines using Python - Work with structured and unstructured data sources - Collaborate with Engineering, Product, and SMEs to deliver AI-driven features - Monitor model performance, drift, and reliability in production - Conduct experimentation, A/B testing, and performance benchmarking - Contribute to architecture design for AI-powered systems Qualifications Required: - Experience: 6-8 Years - Core Stack: Python (advanced proficiency), Machine Learning (supervised/unsupervised learning, NLP), Generative AI (LLMs, prompt engineering, embeddings), RAG architecture and vector search, Model evaluation and validation frameworks - ML & AI Tools: LangChain / LlamaIndex (or similar frameworks), Scikit-learn / XGBoost / LightGBM, PyTorch / TensorFlow, HuggingFace / OpenAI / LLM APIs, Vector databases (Pinecone, FAISS, Weaviate, OpenSearch, etc.) - Data & Infrastructure: SQL and data querying, Experience with AWS / Azure / GCP, CI/CD for ML deployments, Model tracking tools (MLflow preferred) - Good to Have: Experience in legal, regulatory, or publishing domains, Experience with model monitoring and MLOps, Knowledge of fine-tuning techniques (LoRA, PEFT, QLoRA), Experience with multi-agent frameworks (AutoGen, LangGraph, etc.) Additional Company Details: LexisNexis Legal & Qualified provides legal, regulatory, and business information and analytics to help customers increase productivity, improve decision-making, achieve better outcomes, and advance the rule of law worldwide. The company offers various benefits including comprehensive health insurance, enhanced health insurance options, group life insurance, group accident insurance, flexible working arrangements, employee assistance program, medical screening, modern family benefits, long-service awards, new baby gift, subsidized meals in Chennai, various paid time off, and free transport pick up and drop from home-office-home in Chennai. As an equal opportunity employer, qualified applicants are considered and treated without regard to various characteristics protected by law. Role Overview: You will be working as a Sr. Data Scientist at LexisNexis Legal & Professional, focusing on complex projects involving AI, GenAI, RAG, and Agentic Solution for business delivery projects. Your role will involve designing, developing, and deploying machine learning and GenAI solutions in production environments, optimizing Retrieval-Augmented Generation (RAG) pipelines, fine-tuning and evaluating Large Language Models (LLMs), developing prompt engineering strategies, and implementing scalable ML pipelines using Python. You will also collaborate with Engineering, Product, and Subject Matter Experts (SMEs) to deliver AI-driven features, monitor model performance in production, conduct experimentation and performance benchmarking, and contribute to the architecture design for AI-powered systems. Key Responsibilities: - Design, develop, and deploy machine learning and GenAI solutions in production environments - Build and optimize Retrieval-Augmented Generation (RAG) pipelines - Fine-tune and evaluate Large Language Models (LLMs) - Develop prompt engineering strategies and evaluation frameworks - Implement scalable ML pipelines using Python - Work with structured and unstructured data sources - Collaborate with Engineering, Product, and SMEs to deliver AI-driven features - Monitor model performance, drift, and reliability in production - Conduct experimentation, A/B testing, and performance benchmarking - Contribute to architecture design for AI-powered systems Qualifications Required: - Experience: 6-8 Years - Core Stack: Python (advanced proficiency), Machine Learning (supervised/unsupervised learning, NLP), Generative AI (LLMs, prompt engineering, embeddings), RAG architecture and vector search, Model evaluation and validation frameworks - ML & A