Gen AI/ML

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 / AWS Bedrock / GCP Ve

rtex 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, mi

croservices