Lead - Generative AI - Risk Data Management - IIM/ISB/MDI/FMS

Key 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.

Total Experience: 12 - 15 years

Qualification Criteria:

Education:

- Required: B.Tech / B.E. / Dual Degree from a Tier-1 engineering college (IITs, IISc, NITs, BITS) 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.

Professional Experience:

- 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.

Tech Stack Competency:

- 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.