Role & responsibilities
Architect and maintain agentic pipelines (LangGraph, CrewAI, AutoGen).
Implement RAG, prompt engineering, context/memory/state management.
Develop, fine-tune, and evaluate LLM models (Bedrock, OpenAI, Anthropic).
Prototype classical ML models (forecasting, clustering, scoring) when required for hybrid AI systems.
Deploy via SageMaker / ECS / Lambda with observability hooks
Implement guardrails, evaluation metrics, and cost/performance optimization.
Advise BA team on model selection, explainability, and feasibility.
Python backend devloper having understanding of design patterns .

Preferred candidate profile
LangGraph / CrewAI / AutoGen / LangChain / LlamaIndex
Python (async), FastAPI, MLflow, PyTorch / Scikit-learn
LLM fine-tuning (LoRA, PEFT, embeddings, prompt tuning)
Vector DBs: FAISS, Pinecone, Chroma
AWS: Bedrock, SageMaker, Step Functions, ECR
Statistical ML, feature engineering, XAI (SHAP/LIME)

📌 Ai Engineering Lead Bengaluru
🏢 Tata Consultancy Services
📍 Bengaluru