AI Engineering Enablement Director

Role Purpose

Help bring our AI Capability Model to life by turning principles into practical, scalable ways of working. You will enable teams to build secure, responsible, resilient, and cost‑effective AI solutions by creating clear guidance and reusable foundations, and by supporting lean, continuous assurance that helps teams deliver with confidence.

Role Summary

This role operates across AI Governance, AI Engineering and our Center of Excellence supporting business objectives with robust and manageable AI solutions.

What You'll Be Doing

• Turn principles into adoptable practice (Enablement)
  • Translate high‑level architecture and governance guidance into practical, reusable assets that support consistent, scalable delivery.
  • Contribute to defining enabling services that help teams deliver and operate AI solutions safely and reliably.
  • Develop and maintain a library of reference artefacts (templates, examples, checklists) that support effective adoption of recommended practices.
  • Lead and facilitate the AI engineering knowledge and community activities—curating content, running learning sessions, and integrating feedback into improved guidance.
  • Review and adapt industry best practices, working with internal experts to publish reusable patterns and architectural recommendations.
• Make assurance practical, lean, and continuous (Controls)
  • Support and refine a streamlined, evidence‑based assurance approach that provides clear visibility across AI initiatives and their lifecycle.
  • Promote and enable automation of key checks within delivery workflows to help teams meet governance expectations efficiently.
  • Collaborate with architecture, governance, risk, security, product, and finance teams to align standards and close enablement gaps.
  • Ensure engineering practices remain aligned with relevant risk and compliance frameworks through clear, auditable evidence.
• Curate reference assets for speed and consistency
  • Develop and evolve technology and project reference materials that support consistent assessment of fit, risks, and operating considerations.
  • Define and maintain criteria for reusable or endorsed patterns to support clarity and consistency across teams.
• Keep us connected to the market
  • Monitor emerging industry practices, standards, and partner activity to maintain an outside‑in perspective.
  • Translate external insights into practical internal guidance and reusable artefacts for teams.

Outcomes You’ll Drive

• AI initiatives are focused, prioritised, and progress efficiently through a streamlined intake and assessment flow.
• Solutions are secure, resilient, and well‑governed, with risks managed early and proportionately.
• Engineering teams adopt practical standards and reusable patterns, improving quality and delivery velocity.
• AI systems are observable and reliable in production, with behaviour that remains stable over time.
• AI resources are used efficiently and responsibly, supporting sustainable and cost‑aware operation.
• AI development reflects responsible and ethical principles, including fairness, transparency, and strong data stewardship.

What You’ll Bring

• Significant experience in cloud engineering, DevOps, or software delivery (Azure, AWS, or GCP), with a track record of incremental, agile delivery.
• Hands‑on development capability, including practical experience with Python and modern AI frameworks (e.g., LangChain, Semantic Kernel, or similar) to build or support agents, chat interfaces, or retrieval‑augmented solutions.
• Experience applying software engineering fundamentals: writing tests, structuring user stories, managing iterative releases, and working with CI/CD pipelines.
• Experience in AI/ML, software, or platform engineering, with exposure to automated testing and infrastructure‑as‑code or policy‑as‑code.
• Working knowledge of AI observability (logs, metrics, traces, behavioural signals) and practical methods to evaluate or improve AI system behaviour.
• Familiarity with AI risk and governance frameworks (e.g., NIST AI RMF or similar) and the ability to align engineering practices with evidence packs.
• Experience creating or curating engineering enablement assets such as templates, patterns, playbooks, or reusable guidance.
• Strong communication skills, able to explain complex concepts clearly and engage confidently with both technical and non‑technical audiences.
• Ability to collaborate across diverse domains—architecture, security, privacy, product, engineering, and FinOps—using an inclusive and outcome‑focused approach.
• Comfort facilitating knowledge‑sharing sessions, clinics, or community forums.

Nice to have

• Experience contributing to governance or assurance processes, including lightweight control models, intake or assessment flows, or dashboard‑based visibility.
• Exposure to AI FinOps, such as cost‑aware model selection, unit economics, or prompt‑efficiency practices.
• Experience with MLOps or AI delivery tooling, or with AI‑specific observability systems.
• Participation in industry communities or standards bodies, with the ability to translate external practice into internal adoption.
• Experience facilitating workshops or engineering enablement events.
• Familiarity with AI‑specific challenges, such as explainability, drift, data lineage, or safe release practices.
• Understanding of operational quality practices, such as retrieval wiring, guardrails, or policy‑as‑code patterns.

Ways of Working

Operates with a bias toward automation, self‑service, and continuous improvement, using data‑driven decisions and a growth mindset. Acts in a lean, risk‑aware, and responsible way, ensuring trusted outcomes for the business, customers, and society.

Career Stage Director

Equal Opportunity Statement

We are proud to be an equal opportunities employer. This means that we do not discriminate on the basis of anyone’s race, religion, colour, national origin, gender, sexual orientation, gender identity, gender expression, age, marital status, veteran status, pregnancy or disability, or any other basis protected under applicable law. Conforming with applicable law, we can reasonably accommodate applicants’ and employees’ religious practices and beliefs, as well as mental health or physical disability needs.