AI Governance Manager - Risk

ICICI Securities Limited; Navi Mumbai; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

ICICI Securities Limited seeks a specialist to independently evaluate AI/LLM systems, design evaluation frameworks, and establish controls for responsible enterprise AI deployment. You will conduct risk assessments across RAG pipelines, identify data leakage, prompt injection, and hallucinations, and develop governance and testing regimes to ensure robust AI performance and safety.

Work involves benchmarking, golden datasets, LLM-as-a-Judge concepts, and implementing enterprise AI control

Qualifications

• Strong foundation in Generative AI and LLM topics with exposure to risk vectors.
• Experience evaluating AI systems and building governance controls across pipelines.
• Familiarity with RAG workflows, embeddings, and context grounding for robust results.
• Hands-on testing methods for hallucinations, toxicity, calibration, and bias.
• Knowledge of enterprise LLM orchestration platforms such as Bedrock/Azure/Vertex.

Responsibilities

• Independently evaluate AI/LLM systems and design evaluation frameworks.
• Identify risk vectors like data leakage, prompt injection, and context contamination.
• Develop benchmarking tests for hallucinations, fairness, and model calibration.
• Curate datasets and design evaluation test cases with reference answers.
• Implement LLM-as-a-Judge pipelines and governance controls.
• Establish data provenance, sign-off, rate-limiting, and drift monitoring.
• Audit AI outputs and assess performance against evaluation criteria.
• Support enterprise AI control frameworks and post-deployment observability.
• Evaluate tooling like Ragas, TruLens, and DeepEval for AI assessment.

Skills

Generative AI & LLMs

RAG pipelines

AI risk assessment

AI governance

NLP evaluation

Education

B.Tech / M.Tech in CS / Quantitative / MCA

Tools

Amazon Bedrock

Azure OpenAI Service

Vertex AI

Python

Job description

Job Summary

The role will be responsible for independently evaluating AI/LLM systems, identifying potential risk vectors, designing robust evaluation frameworks, and establishing controls to support responsible and secure enterprise AI deployment.

Roles & Responsibilities

• Conduct RAG & GenAI risk assessments across end-to-end RAG pipelines, including knowledge-base ingestion, chunking, embeddings, vector retrieval, context grounding, and generation.
• Identify AI/LLM risks such as data leakage, prompt injection, context contamination, retrieval failures, and hallucinations.
• Design and execute LLM evaluation and benchmarking frameworks to assess hallucination rates, toxicity, robustness, model calibration, fairness, and bias.
• Curate golden/reference datasets and design evaluation test cases with reference answers.
• Implement and validate LLM-as-a-Judge evaluation pipelines.
• Establish AI governance and risk mitigation controls covering data preprocessing, pre-deployment validation gates, and post-deployment observability.
• Independently inspect, evaluate, and validate AI/LLM model outputs.
• Conduct technical audits of AI/LLM systems and assess model performance against defined evaluation criteria.
• Support the development of enterprise AI control frameworks covering data provenance, model sign-off, rate-limiting, and continuous drift monitoring.
• Evaluate automated AI assessment and hallucination detection tooling, including Ragas, TruLens, and DeepEval.

Qualifications

• B.Tech / M.Tech in Computer Science, Quantitative disciplines, or MCA.
• Strong quantitative and/or Computer Science foundation.
• Thorough understanding of AI/ML taxonomy and modern Generative AI workflows.
• Deep understanding of RAG architecture and associated risks, including vector search, embeddings, relevance scoring, and context grounding/grounding boundaries.
• Hands-on familiarity with reference-based testing, LLM-as-a-Judge frameworks, red-teaming fundamentals, and semantic similarity evaluation.

Required Technical Skills:

• Strong expertise in Generative AI / GenAI and Large Language Models (LLMs).
• Strong understanding of Retrieval-Augmented Generation (RAG) pipelines.
• Experience in LLM evaluation, model validation, AI risk assessment, and AI governance.
• Understanding of AI risk vectors including prompt injection, hallucination, data leakage, and retrieval failures.
• Familiarity with enterprise LLM orchestration and deployment platforms such as Amazon Bedrock, Azure OpenAI Service, and Vertex AI.

Preferred / Good-to-Have Skills:

• Working knowledge of Python for querying APIs, parsing JSON outputs, and independently auditing model evaluation scripts.
• Practical experience with NLP evaluation metrics such as ROUGE, BLEU, BERTScore, and semantic distance metrics.
• Ability to construct enterprise AI control frameworks covering data provenance, model sign-off, rate-limiting, and continuous drift monitoring.
• Familiarity with automated hallucination detection and AI evaluation tools such as Ragas, TruLens, and DeepEval.