AI Engineering Enablement Director in London

LSEG (London Stock Exchange Group) is more than a diversified global financial markets infrastructure and data business. We are dedicated, open-access partners with a dedication to excellence in delivering the services our customers expect from us. With extensive experience, deep knowledge and worldwide presence across financial markets, we enable businesses and economies around the world to fund innovation, manage risk and create jobs. It’s how we’ve contributed to supporting the financial stability and growth of communities and economies globally for more than 300 years.

Through a comprehensive suite of trusted financial market infrastructure services – and our open-access model – we provide the flexibility, stability and trust that enable our customers to pursue their ambitions with confidence and clarity. LSEG is headquartered in the United Kingdom, with significant operations in 70 countries across EMEA, North America, Latin America and Asia Pacific. We employ 25,000 people globally, more than half located in Asia Pacific.

OUR PEOPLE: People are at the heart of what we do and drive the success of our business. Our culture of connecting, creating opportunity and delivering excellence shape how we think, how we do things and how we help our people fulfil their potential. We embrace diversity and actively seek to attract individuals with unique backgrounds and perspectives. We break down barriers and encourage teamwork, enabling innovation and rapid development of solutions that make a difference. Our workplace generates an enriching and rewarding experience for our people and customers alike. Our vision is to build an inclusive culture in which everyone feels encouraged to fulfil their potential.

We know that real personal growth cannot be achieved by simply climbing a career ladder – which is why we encourage and enable a wealth of avenues and interesting opportunities for everyone to broaden and deepen their skills and expertise. As a global organisation spanning 70 countries and one rooted in a culture of growth, opportunity, diversity and innovation, LSEG is a place where everyone can grow, develop and fulfil your potential with meaningful careers.

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 the AI Governance, AI Engineering and our centre of excellence supporting our business objectives with robust and manageable AI solutions.

WHAT YOU'LL BE DOING:

• 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.
• 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.
• 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.
• 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.
• 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.