Enterprise AI and Transformation Manager

Enterprise AI & Transformation Manager

Team Transformation

Location AMAZE Central - Holborn

County Greater London

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Closing Date 30-Sep-2026

At Metro Bank, we believe the best banking experience starts with people who genuinely care. We’re not just delivering banking services - we’re building trust through authentic connections. Here, our people come first; our colleagues are part of a team that values individuality, collaboration, and long-standing relationships. We are also all about balance so most of our jobs offer the opportunity for hybrid working built around your role and home life, wherever possible.

As an Enterprise AI Transformation Manager, you will own AI products end to end: from the first problem statement, through solution design and requirements, into build with our AI Build colleagues, and out to live use and measured value. You will work at the point where a reimagined process becomes a working agent, designing what gets built, proving it works, and getting it into the hands of colleagues and customers.

The role mixes product, research, build and analysis. You will spend as much time in the tooling and the data as you will with stakeholders.

• Own AI products end to end: define the problem, size the value, design the solution, and stay with it through build, release and colleague adoption
• Design solutions and write the requirements behind them, covering process flows, agent behaviour, data and integration needs, acceptance criteria and how success will be evidenced, designing for reuse where the same pattern will serve more than one journey
• Partner with our AI Build colleagues and platform capabilities to take those designs into build and deployment, staying close through delivery, testing and release rather than handing over and stepping away
• Work with stakeholders across business, technology, data and risk to shape solutions colleagues will actually use, and hold the line on scope when it matters
• Manage external partners and vendors: brief them properly, run proofs of concept, and hold them to what they promised
• Work hands-on: prototype agents, write and test prompts, design evaluation sets, and interrogate outputs before anything reaches a colleague or a customer
• Track what is emerging in AI and agentic tooling, run structured evaluations, and make evidenced recommendations on what we should test, stop or scale
• Build the value case and then prove it: baseline the process, instrument the product, and report the outcome honestly, including where it fell short
• Design controls, guardrails and evaluation in from the outset, so we can move quickly inside a regulated environment rather than slowly alongside it

• Hands-on experience building or delivering AI products, whether LLM applications, agents, automation or ML, with a real view of what holds up in production
• A track record of designing solutions and writing the requirements engineers build from, then staying with delivery through to release
• Working fluency with current AI tooling: prompting and prompt evaluation, retrieval, agent frameworks, API-level work, and at least one cloud AI platform. Python or similar is a strong advantage
• Product instinct. You can take a vague business problem, find the job that actually needs doing, and decide what to build and what to leave out
• Strong analytical ability. You can build a value model, source and sense-check the data behind it, and defend the numbers under challenge
• Confident with both sides of the table. You can run a working group of senior internal stakeholders and hold an external vendor to account
• A research habit. You read the releases, run your own tests, and form your own view rather than repeating vendor claims
• Experience delivering in a regulated or…