Found Description
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 system s. 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 safe and compliant GenAI outp uts. Work with open-source and proprietary models (OpenAI, Gemini, Llama, Claude, Mistral, Falcon, e tc.).Proficiency with Python, LangChain / LlamaIndex, HuggingFace, PyTorch, Tensor Flow. Experience with RAG pipelines, embedding models, vector databases (FAISS, Pinecone, Milvus, Chroma, OpenSe arch). Hands-on with agentic AI frameworks (LangGraph, Autogen, CrewAI, Haystack Agents, Swarm). Experience with Azure OpenAI / AW...