Data Scientist (Senior Lead) (Telangana)

Data Scientist (Senior Lead) Experience: 15 to 20+ years Function: AI & Machine Learning Location: Cognizant India (Hybrid) ROLE SUMMARY Owns the ML and GenAI model strategy for the product. Leads RAG design, hybrid retrieval, model evaluation with LLM-as-a-Judge, and LLM fine-tuning programmes. Sets the standard for how agents retrieve, reason, and generate reliable outputs. KEY RESPONSIBILITIES - Define and own the ML/GenAI model strategy for all agentic use cases on the platform - Lead RAG pipeline design, hybrid retrieval strategy, and vector/graph database selection - Establish model evaluation frameworks: benchmarking, accuracy, LLM-as-a-Judge calibration - Lead fine-tuning programmes: LoRA, QLoRA, PEFT, DPO, SFT, RFT (Reinforcement Fine-Tuning) - Guide the ML Engineer on MLOps practices, model promotion, and serving optimisation - Run structured A/B tests, model comparisons, and prompt strategy evaluations - Produce model cards, evaluation reports, and executive-level performance insights TECHNICAL SKILLS - Languages & Libraries: Python; PyTorch, TensorFlow, scikit-learn, Hugging Face Transformers, LlamaIndex - LLMs & Fine-Tuning: LoRA, QLoRA, PEFT, RFT, RLHF, DPO, SFT, instruction tuning - RAG & Hybrid Retrieval: BM25 + dense vector, reranking (Cohere Rerank, ColBERT, cross-encoders) - Vector & Graph Databases: Pinecone, pgvector, Weaviate, Qdrant; Neo4j, Memgraph - Evaluation: Ragas, DeepEval, HELM, PromptFoo, TruLens; LLM-as-a-Judge design and calibration - MLOps: MLflow, Weights & Biases, experiment tracking, model registry, drift monitoring - Cloud AI: AWS SageMaker, Azure ML, GCP Vertex AI - Statistics: experiment design, A/B testing, causal inference, significance testing NICE TO HAVE - Published research at NeurIPS, ICML, ACL, or equivalent - Agentic AI framework experience (LangGraph, AutoGen) - Responsible AI tooling: Fairlearn, AI Fairness 360, Giskard - Knowledge graph and LLM knowledge pipeline design experience .