Job Title

Location : Pan IndiaExo : 7- 15 YrsBuild, 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 safe 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, TensorFlow.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