1. Build, fine-tune, and optimize Large Language Models (LLMs) for banking-specific use cases (credit risk, AML, KYC, audit, compliance, customer service, etc.).
2. Develop RAG pipelines, vector databases, embeddings, and multimodal retrieval systems.
3. 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.).
4. Implement prompt engineering, prompt-chaining, and guardrails for secure and compliant GenAI outputs.
5. Work with open-source and proprietary models (OpenAI, Gemini, Llama, Claude, Mistral, Falcon, etc.).
6. Proficiency with Python, LangChain / LlamaIndex, HuggingFace, PyTorch, TensorFlow.
7.



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

📌 Gen AI ML (Pune)
🏢 Tata Consultancy Services
📍 Pune