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full-timeSenior AI Engineer
Multiverse · London
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 StepStone 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. The AI Transformation team 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. What your day-to-day will look like 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. What We Are Looking For Production AI Agent Engineering: You have shipped AI systems that serve real users at scale. You 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 can explain technical concepts clearly to both engineers and business colleagues. You proactively engage the people around your work, and bring them along with you on the journey Fluent in AI-assisted development and can mentor others to do it well 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 with the exception of Coaches and Instructors who collaborate in the office once a month 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 sa
| Role | Senior AI Engineer |
|---|---|
| Company | Multiverse |
| Location | London |
| Compensation | Not disclosed |
| Deadline | Rolling |
Typical process for this type of role
A general guide — the exact steps for this specific listing may vary; check the original posting for details.
- 1ApplicationSubmit your resume through the apply link.
- 2ScreeningRecruiter reviews your background against the role.
- 3AssessmentA technical test, assignment, or coding round, depending on the role.
- 4Interview(s)One or more rounds with the hiring team.
- 5OfferOffer letter with compensation and start date.
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