Ai Model Governance Specialist

Job description

We require a seasoned technical person who has experience and understanding of the AI Model who would assist us in smooth implementation of the AI models into our systems. The person will oversee the risk management, validation, and control lifecycle for Generative AI and Large Language Model (LLM) deployments across enterprise systems. This role bridges technical engineering with governance to ensure AI models specifically Retrieval-Augmented Generation (RAG) architectures are safe, compliant, explainable, and reliable. Roles and Responsibilities: • Working knowledge of AI/ML taxonomy • Solid understanding of RAG architecture Knowledge Base ingestion, embedding/retrieval, grounding, and generation stages, and where risk arises at each • Familiarity with LLM evaluation methods and metrics hallucination detection, toxicity, robustness, calibration, fairness/bias testing, LLM-as-judge approaches, and reference-answer/golden dataset construction • Understanding of the logic behind evaluation tools like hallucination detectors, accuracy tools, etc. • Awareness of major cloud/AI platforms used for enterprise LLM deployment (e.g. Amazon bedrock or equivalent) • Ability to design control frameworks spanning data, model validation, deployment and monitoring • Working knowledge of Python to independently inspect evaluation outputs