Senior AI Engineer

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.

In April 2026, we announced $70 million in strategic funding, led by Schroders Capital, with participation from Step Stone Group, Lightspeed Venture Partners and General Catalyst. At an increased valuation of $2.1bn, the round makes us Europe’s first EdTech double unicorn.

But we aren’t stopping there. With a strong operational footprint and 800+ employees, we have ambitious plans to continue scaling. We’re building a world where tech skills unlock people’s potential and output.
Join Multiverse and power our mission to equip the workforce to win in the AI era.

AI Transformation is a small, focused squad that is driving Multiverse’s next wave of growth. Our mandate is 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.

But our work doesn’t stop there. We are building a product that we will take to market to truly be a transformation partner to our customers. Joining this team means shaping the future of Multiverse, of our customers, and of the wider workforce as we navigate the new world of work.

The team is high velocity, inherently cross-functional and broad in scope. We work alongside colleagues whose work we are reimagining, and closely with the wider engineering org building Multiverse's current customer-facing product. The structure is flat and fast, with egos left at the door, bias to action and fun at our core.

Every day will be different, but you can expect it to include:

Own and deliver end-to-end AI systems: agents and the platforms they depend on. You identify a user problem, break it down and build the AI system that solves it, in close partnership with the teams who will adopt it

Design context and retrieval strategies:the retrieval pipelines, conversation memory, summarisation strategies, chunking logic and RBAC that make context useful and permission-aware

Build evaluation frameworks.You build automated eval pipelines and human-in-the-loop review processes that tell the team whether its AI systems are doing what they should.

Design tool integrations.Agents are only as capable as the systems and context they can reach. You design and build the tool layer: MCPs, APIs, data contracts, and the error handling that makes tool use reliable.

Influence technical direction.You have opinions about how things should be built, and you back them up with evidence. You contribute to architectural decisions, push back when the team is heading in the wrong direction, and propose better approaches.

• Production AI Agent Engineering:You have shipped AI systems that serve real users  understand the engineering challenges that make agent systems different from conventional software, including context management, model selection and routing, cost engineering, tool use and agent augmentation, and evaluation

• Product mindset:Your natural instinct is to identify the real user pain, understand their requirements and build pragmatic, high-impact solutions. You test, learn and iterate fast, and are comfortable executing under high ambiguity

• Full-Stack Delivery:You work across the stack: LLM integration, backend services, data pipelines, and enough frontend to ship end to end

• Communication:You…