Exp : 7- 15 Yrs

Build, fine-tune, and optimize Large Language Models (LLMs) for banking-specific use cases (credit risk, AML, KYC, audit, compliance, customer service, etc.)
Develop RAG pipelines, vector databases, embeddings, and multimodal retrieval systems.
Design and integrate agentic AI workflows, autonomous agents, and multi-agent orchestration for complex banking tasks (case investigation, decision reasoning, trade surveillance alerts, credit assessment, etc.)
Implement prompt engineering, prompt-chaining, and guardrails for protected and compliant GenAI outputs.
Work with open-source and proprietary models (OpenAI, Gemini, Llama, Claude, Mistral, Falcon, etc.)
Proficiency with Python, LangChain / LlamaIndex, HuggingFace, PyTorch, Tensor Flow.




Experience with RAG pipelines, embedding models, vector databases (FAISS, Pinecone, Milvus, Chroma, OpenSearch)
Hands-on with agentic AI frameworks (LangGraph, Autogen, CrewAI, Haystack Agents,Swarm)
Experience with Azure OpenAI / AWS Bedrock / GCP Vertex AI
Machine learning experience across supervised, unsupervised, time-series,and NLP
Strong knowledge of ML Ops & LLM Ops (Sagemaker, Vertex AI, Azure ML, MLflow,Kubeflow)
Understanding of DevOps, CI/CD, Docker, Kubernetes, microservices

📌 Gen AI/ML (Chennai)
🏢 Tata Consultancy Services
📍 Chennai