AI Engineering Enablement Director

Requirements

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

What the job involves

• 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.
• This role operates across the AI Governance, AI Engineering and our centre of excellence supporting our business objectives with robust and manageable AI solutions.
• You will turn our AI architecture and governance principles into practical enablers for teams—creating the clarity, reusable foundations, and lean assurance needed to support high‑quality delivery, steady velocity, and responsible growth across the organisation.

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.