AI Governance Engineer

Technical AI Governance SME: Provide support to delivery teams adopting AI-enabled delivery tools (ex: agentic coding solutions, agentic workflows).

Proactive AI Governance Engagement: Translate AI Governance requirements into engineering-ready guardrails, evidence expectations, mitigation options, decision rationale, and approval conditions to Portfolio Execution Teams and other relevant business areas during initial ideation, proof of concept, pilot, and pre-AI Governance approval efforts.

Cross-Functional Coordination: Collaborate with AI Security and other Security verticals to ensure that their specific requirements are accounted for and represented in engineering-ready guardrails and mitigations.

AI Governance Review Support: Collaborate with AI Working Group members to assess AI solutions leveraged in software development and other designed high-risk AI initiatives. Facilitate alignment across Engineering, Security, Architecture, Privacy, Legal, Compliance, Portfolio Execution, IT/IS Risk, and business teams to identify risk mitigation requirements, resolve issues, clarify ownership, and move initiatives through review.

Formalize AI Governance Requirements: Convert AI Governance expectations, SME inputs, and review outcomes into clear, actionable requirements, guardrails, approval conditions, and implementation guidance for delivery teams.

Identify requirements from AI Governance Working Group stakeholders and SMEs for the domains they own, including AI Security, Privacy, Legal, Compliance, Architecture, IT/IS Risk, and other relevant functions, and ensure those requirements are accurately reflected in holistic AI governance guidance and process documents.

Create process documents, checklists, FAQs, and decision materials that help delivery teams, SMEs, and leaders understand governance expectations, pilot guardrails, review outcomes, and required next steps.

7-10 years of experience in software engineering, technology delivery, architecture, security engineering, or related fields.

Strong communication skills, with the ability to work credibly with technical teams and translate technical risk into business, governance, compliance, and executive-facing language.

Interest in AI Governance and AI Risk. Able to turn AI risks into clear delivery requirements, including required controls, evidence, mitigations, approval conditions, and audit-ready rationale.

Working knowledge of AI coding assistants, GenAI tools, agentic workflows, prompt patterns, model/tool limitations, data leakage risks, human oversight, monitoring expectations, and responsible AI practices.

Hands-on technical background with understanding of application architecture, SDLC controls, CI/CD, APIs, cloud platforms, access models, data flows, and implementation tradeoffs.