AI Governance Engineer

Position Overview
The AI Governance Engineer supports the implementation and continual maturation of the organisation's AI governance framework in alignment with the strategic direction, policies and risk appetite defined by the business.
The role will translate governance requirements into practical controls, standards, processes, technical guardrails and evidence mechanisms that can be consistently applied across the AI lifecycle.
The role will support two core AI governance pillars:
1. Self-Service AI, enabling employees and business teams to safely adopt approved AI capabilities within defined governance guardrails.
2. Enterprise Agentic Build Processes, embedding governance, security, assurance and Responsible AI requirements into the design, development, testing, deployment and operation of enterprise AI agents and AI-enabled solutions.
Under the direction of the AI Governance Lead, the AI Governance Engineer will assist in implementing governance controls, standards, processes and technical guardrails across AI platforms, self-service AI capabilities and enterprise agentic delivery initiatives.
The role is primarily implementation focused. Strategic direction, policy intent and risk appetite will be established by the appropriate business and governance stakeholders, with the AI Governance Engineer responsible for translating that direction into repeatable and operational governance practices.
Key Responsibilities
• Implement and continually mature the AI governance framework across Self-Service AI and Enterprise Agentic Delivery capabilities.
• Translate governance requirements into practical controls, standards, templates, guardrails and implementation patterns that can be consistently applied across AI platforms and solutions.
• Work alongside AI developers and platform teams to embed governance requirements into development standards, solution lifecycle processes and deployment practices.
• Support the implementation of Responsible AI, security, privacy and data governance requirements across AI solutions and platforms.
• Support the implementation and ongoing operation of governance processes covering AI solution intake, risk assessment, approvals, monitoring and periodic review.
• Implement reporting and monitoring capabilities that provide visibility of AI adoption, compliance, risk, usage and cost transparency.
• Under the direction of the AI Governance Lead, work with Security, Privacy, Legal, Architecture, Data Governance and Engineering teams to implement and maintain approved AI governance controls, standards and processes.
• Provide practical guidance and support to business and engineering teams adopting AI capabilities.
• Maintain governance artefacts, records, standards and supporting documentation required to demonstrate compliance and operational effectiveness.
• Identify opportunities to improve governance processes, controls, tooling and adoption as AI capabilities mature across the organisation.
Desired Experience
• Experience in technology governance, risk, security, data governance, compliance or a related discipline.
• Understanding of AI technologies, including generative AI, copilots, large language models and emerging agentic AI concepts.
• Experience implementing controls, standards or governance processes within technology delivery environments.
• Familiarity with cloud and AI platforms such as Agent 365, Azure AI Foundry, Azure OpenAI, Microsoft Copilot, GitHub Copilot, Databricks Genie or similar technologies.
• Ability to work collaboratively with engineering, security, architecture and business teams.
• Strong analytical, documentation and communication skills.
• Experience with automation, APIs, low-code development or AI solution delivery is desirable.
What We’re Looking For
We are looking for someone who can bridge governance and technology in a practical, delivery-focused environment. The ideal candidate is technically curious, collaborative and comfortable working alongside developers, architects and business stakeholders to implement governance standards, controls and guardrails that enable responsible AI adoption at scale. This individual will help Data Foundations establish sustainable governance practices while supporting innovation and business value.
Measures of Success
• AI governance requirements are successfully implemented through practical controls, standards, processes and technical guardrails aligned with business objectives.
• Governance is consistently embedded across both Self-Service AI and Enterprise Agentic Delivery processes, enabling responsible AI adoption at scale.
• AI solutions maintain appropriate ownership, risk classification, approvals, control evidence and lifecycle governance records.
• Governance reporting provides clear visibility of AI adoption, compliance, risks, exceptions, usage and cost transparency, with issues identified and managed proactively.
• Business and engineering teams can confidently adopt and develop AI solutions using established governance frameworks, standards and implementation guidance.

Work arrangement
Yes