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.

Experience & Skills

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.