AI Governance

ABOUT:Makes AI systems trustworthy and compliant — governance frameworks, model risk assessment, and regulatory implementation that keep delivery out of legal and reputational trouble.KEY RESPONSIBILITIESDesign and implement AI governance frameworks and policy for client engagementsConduct model risk assessments and responsible-AI reviews before production go-liveOwn AI regulatory implementation — mapping engagement requirements to relevant regulation (EU AI Act, sector rules)Design and run AI red-teaming exercises for high-risk systemsOwn AI testing & evaluation standards from a governance/ethics lens (bias, fairness, explainability, AI ethics)Advise practice leads and Architects on governance risk during solution design, not just at the endFacilitate cross-functional governance reviews across legal, technical, and business stakeholdersBe the voice that slows down a launch when risk warrants it, under commercial pressureTrain delivery teams on responsible-AI practicesREQUIREMENTS & SKILLS6–12+ yrs in AI governance, model risk, compliance, or responsible-AI rolesWorking knowledge of AI regulation (EU AI Act, NIST AI RMF, sector-specific frameworks) and how to operationalize itTechnical enough to critically assess a model card or evaluation report, not just check a boxFamiliarity with bias/fairness testing methodologies and explainability toolingFamiliar with platform-native AI safety/governance tooling — Microsoft Azure AI Content Safety, AWS Bedrock Guardrails, and Google Vertex AI safety filters; open-source evaluation tooling (Giskard, DeepEval) a good-to-haveExperience designing red-team protocols for AI systemsStrong written policy/framework authorship — produces documents legal and technical teams both trustComfortable being the voice that slows down a launch when risk warrants it, under commercial pressureFacilitates cross-functional governance reviews across legal, technical, and business stakeholdersDiplomatic but firm — holds the line on risk without becoming an obstructionist