Role & responsibilities

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

Preferred candidate profile

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

📌 AI Engineering lead (Bengaluru)
🏢 Tata Consultancy Services
📍 Bengaluru