Senior AI Engineer - AI Transformation

Multiverse is the upskilling platform for AI and Tech adoption. We have partnered with 1,500+ companies to deliver a new kind of learning that’s transforming today’s workforce. Our upskilling apprenticeships are designed for people of any age and career stage to build critical AI, data, and tech skills. Our learners have driven $2bn+ ROI for their employers, using the skills they’ve learned to improve productivity and measurable performance.

The Role

Multiverse is the UK’s largest apprenticeship provider and its first EdTech unicorn. The current state of AI presents a huge opportunity to reshape the future of education and workforce development. Multiverse is in a uniquely strong position to do that, and getting it right has implications beyond the company: for the UK tech sector and the broader economy. The AI Transformation team exists to make that real, starting with Multiverse itself. This is not a team that bolts AI onto the edges of the business or ships a handful of internal productivity tools. The mandate is bigger: to rebuild how the company actually works, function by function, and to establish the practices that make Multiverse an AI‑first company from the core out.

You are not just changing one company, you are building the blueprint others will follow. The team is one small, focused squad, accountable for outcomes end to end. You work closely with the wider engineering org building Multiverse’s customer‑facing product, and alongside the teams whose work you are helping to reinvent. The structure is flat and fast. No shared queues, no bureaucratic overhead between having an idea and shipping it. Whilst we are building something entirely new, Multiverse has an established product, existing infrastructure, and engineering teams in London and Berlin. You need to be as comfortable integrating existing systems and working across team boundaries as you are building new ones from scratch.

What You Will Do

• Own and deliver complete agent systems. You take a product problem and build the agent system that solves it. Architecture, implementation, evaluation, and production operation.
• Design context and retrieval strategies. What goes into the context window and what stays out is the most consequential design decision in an AI system.
• Build evaluation frameworks. You define and implement the metrics that tell the team whether its AI systems are doing what they should.
• Design tool integrations. Agents are only as capable as the systems they can reach.
• Influence technical direction. You have opinions about how things should be built, and you back them up with evidence.
• Raise the bar through code review and pairing. You review code with rigour and give feedback that makes the team better.
• Use Claude Code as your primary development workflow.

What We Are Looking For

• Production AI Agent Engineering: Context management, model selection and routing, cost engineering, tool use and agent augmentation, evaluation.
• Product Thinking: You do not wait for a spec. You understand the problem, figure out what needs to exist, and build it.
• Full‑Stack Delivery: You work across the stack: LLM integration, backend services, data pipelines, and enough frontend to ship end to end.
• Communication: You explain technical decisions clearly to both engineers and the product and design people you work with day to day.

What Would Set You Apart

• Experience building AI systems in EdTech, regulated content, or domains where output quality has compliance or accreditation implications.
• Background as a founding or early‑stage engineer at a startup.
• Published thinking or external contributions in AI engineering (talks, writing, open source).
• Experience with multi‑agent coordination: task decomposition, handoff, shared state.

What We Are Not Looking For

• Pure ML researchers without production engineering experience.
• Narrow specialists.
• Engineers who need a detailed spec and a sprint plan before starting.
• Candidates whose AI experience stops at wrapping LLM APIs.
• Engineers who optimise for technical elegance over user outcomes.

Benefits

• Time off - 27 days holiday, plus 5 additional days off: 1 life event day, 2 volunteer days, 2 company‑wide wellbeing days (M‑Powered Weekend) and 8 bank holidays per year.
• Health & Wellness - private medical Insurance with Bupa, a medical cashback scheme, life insurance, gym membership & wellness resources through Wellhub and access to Spill - all in one mental health support.
• Hybrid work offering - for most roles we collaborate in the office three days per week.
• Work‑from‑anywhere scheme - you’ll have the opportunity to work from anywhere, up to 10 days per year.
• Space to connect: Beyond the desk, we make time for weekly catch‑ups, seasonal celebrations, and have a kitchen that’s always stocked!

Our Commitment to Diversity, Equity and Inclusion

We’re an equal opportunities employer. And proud of it. Every applicant and employee is afforded the same opportunities regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. This will never change.

Our Commitment to Safeguarding

Multiverse is committed to safeguarding and promoting the welfare of our learners. We expect all employees to share this commitment and adhere to our Safeguarding Policy, our Prevent Policy and all other Multiverse company policies.