Help bring our AI Capability Model to life by turning principles into practical, scalable ways of working
Enable teams to build secure, responsible, resilient, and cost‑effective AI solutions by creating clear guidance and reusable foundations
Support lean, continuous assurance that helps teams deliver with confidence
Turn our AI architecture and governance principles into practical enablers for teams—creating clarity, reusable foundations, and lean assurance needed to support high‑quality 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
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 meet governance expectations efficiently

Significant experience in cloud engineering, DevOps, or software delivery (Azure, AWS, or GCP)
Hands‑on development capability, including practical experience with Python and modern AI frameworks (e.g., LangChain, Semantic Kernel, or similar)
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)
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.

AI Engineering Enablement Director

Key skills