AI Engineering Manager

The role

Every team has AI. Few have mastered it. Humans were built to adapt, but AI is now moving faster than most workforces can.

Multiverse is the AI adoption layer: the missing link between the AI tools that companies have invested in and the people expected to use them.

Our ambition is to put AI capability in every worker’s hands. Apprenticeships got us started: we’ve trained over a third of the UK’s data and AI apprentices, worked with a third of the FTSE 100, and generated £2bn+ in verified ROI. Now Mastry, our new adaptive learning platform, takes that further, guiding a company’s AI workforce transformation from start to finish. Mae, our AI guide inside Mastry, is a real-time AI agent that thinks, speaks, listens and sees, working alongside the tools you already use, like Claude and Copilot.

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AI Engineering Manager

Multiverse, London

The role

Every team has AI. Few have mastered it. Humans were built to adapt, but AI is now moving faster than most workforces can.

Multiverse is the AI adoption layer: the missing link between the AI tools that companies have invested in and the people expected to use them.

What you will do

You will own delivery, technical direction, and team health in the D&P service estate — the full learner and customer journey from diagnosis through prescription to onboarding and growth tracking. Concretely, that means leading the team(s) across surfaces like:

Diagnosis & needs assessment – AI-heavy services (retrieval-augmented generation (RAG), LLM-scored assessment) that turn raw signal into a picture of where a learner or employer actually is.
Prescription & eligibility – build the product which helps learners and customers understand the best learning course for their skills and career development, that leverages a customer's levy funds. Much of this is load-bearing logic with a genuine financial and regulatory blast radius.
Recommendation & onboarding – turning a diagnosis into the right prescribed path and getting learners into it, where those decisions become visible to customers and learners.
The platform arc – continuing to carve D&P logic out of the shared legacy monolith into dedicated services, while backing the newer AI-native product from beta toward scale.
Coach and build the team. Hire well, give every report a real growth plan, and move the team into a high-performing, AI-native operating rhythm — promotions and performance issues are never a surprise.
Stay hands-on. Whiteboard distributed designs, review and negotiate the RabbitMQ event schemas and API contracts that ripple across services, and write production code yourself when it's the fastest way to de-risk or unblock.
Deliver predictably. Use cycle time, change failure rate, and MTTR to balance shipping speed, debt, and change-safety on logic with genuine financial and regulatory blast radius.
Raise the AI-native bar. Prove better discovery-to-build-to-review loops on your own team and share what works across engineering.
Partner on strategy. Work directly with Product and Design — and closely with go-to-market — to turn learner and customer behaviour into technical direction on the surfaces where eligibility and recommendation decisions become visible to real customers.

AI Engineering Manager

The role

AI Engineering Manager

The role

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Multiverse