Job description

Role & responsibilities:

GenAI Strategy: Identify, blueprint, and execute Large Language Model (LLM) use cases like automated credit appraisal memo (CAM) generation, multi-source financial analysis, and AI-powered operational risk loss incident tagging.
Horizontal Execution: Act as the AI consultant for all 7 risk verticals, with an immediate focus on Retail Credit (automated underwriting, early warnings) and Operational Risk (anomaly detection, control testing automation).
Team Leadership: Manage and mentor a team of Risk Data Engineers and elite IIT freshers, bridging academic AI knowledge with practical banking applications.
Pipeline Collaboration: Work closely with the Risk Data Management infrastructure to ingest, clean, and structure unstructured bank data for LLM fine-tuning and Retrieval-Augmented Generation (RAG) pipelines.
RBI Compliance & Safety: Mitigate risks specific to GenAI, including hallucination management, data privacy (PII masking), model bias, and alignment with Reserve Bank of India (RBI) AI governance norms.

Preferred candidate profile

• B.Tech / B.E. / Dual Degree in Computer Science, Data Science, Quantitative Economics, or a related field.

•Preferred: Certifications in Deep Learning/NLP or professional risk credentials (FRM/CFA) are a strong plus.

•Total Experience: 8 to 12 years in analytical or technology transformation roles.

•GenAI/LLM Focus: Minimum 2+ years of hands-on experience building, fine-tuning, or deploying LLM frameworks (e.g., using LangChain, LlamaIndex, vector databases) in a corporate environment.

•Domain Expertise: Strong exposure to financial services, preferably with a deep conceptual grasp of Retail Credit underwriting or Operational Risk frameworks.

•People Management: Prior experience managing young, highly analytical engineering talent (like IIT/NIT grads) and aligning them with business goals.

•GenAI Ecosystem: Python, PyTorch, Hugging Face, OpenAI API/Open-Source LLMs (Llama, Mistral), Vector DBs (Pinecone, Chroma, Milvus).

•Data Engineering: SQL, Spark, cloud data warehouses (Snowflake, Databricks), and orchestration tools