Senior AI Engineer – AI Transformation

Multiverse

Senior AI Engineer developing products for AI transformation at Multiverse, the UK's largest apprenticeship provider. Leading AI integration while enhancing operational efficiency and learning outcomes.

Posted 6/18/2026full-timeLondon • 🇬🇧 United KingdomSeniorWebsite

About the role

Key responsibilities & impact

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. You are responsible for the system working, not just for your code compiling.
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. You design retrieval pipelines, conversation memory, summarisation strategies, and the chunking logic that makes context useful rather than noisy. You understand the cost and quality trade-offs at every layer.
Build evaluation frameworks. You define and implement the metrics that tell the team whether its AI systems are doing what they should. Accuracy, safety, helpfulness, domain-specific quality, latency. You build automated eval pipelines and human-in-the-loop review processes. You treat evaluation as an engineering discipline, not an afterthought.
Design tool integrations. Agents are only as capable as the systems they can reach. You design and build the tool layer: MCPs, APIs, data contracts, and the error handling that makes tool use reliable. You work closely with the wider engineering org building Multiverse's customer-facing product, whose systems your agents need to interact with.
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. You are not a team lead, but your technical judgement shapes what gets built and how.
Raise the bar through code review and pairing. You review code with rigour and give feedback that makes the team better. You pair with less experienced engineers on hard problems. You set a standard for what production-quality AI engineering looks like.
Use Claude Code as your primary development workflow. Claude Code is how this team builds. You set context, define constraints, review output critically, and augment the tool with skills and domain context. You are fluent in AI-assisted development and can mentor others in doing it well.

Requirements

What you’ll need

Production AI Agent Engineering
Have shipped AI systems that serve real users at meaningful scale.
You understand the engineering challenges that make agent systems a different discipline from conventional software
Context management. Designing what enters the context window and what stays out. Retrieval strategies, chunking approaches, conversation memory, summarisation. You know how context quality drives output quality and cost, and you have made these trade-offs in production.
Model selection and routing. Choosing the right model for a task based on capability, latency, cost, and reliability. You have worked with multiple models and understand when a smaller, faster model is the right call.
Cost engineering. Token economics, caching, prompt optimisation, batching. You know the difference between a prototype that works and a production system that works at a cost the business can sustain.
Tool use and agent augmentation. Designing the tool surfaces that agents use to interact with external systems. Writing tool descriptions that models use reliably, handling failures gracefully, building integration layers that are composable rather than brittle.
Evaluation. Building frameworks for assessing AI output quality: accuracy, safety, helpfulness, domain-specific criteria. You ship with eval, not after it.
Product Thinking
You do not wait for a spec. You understand the problem, figure out what needs to exist, and build it. On a small squad there is no gap between product thinking and engineering. You talk to users, understand their workflows, and identify the highest-value intervention.
Full-Stack Delivery
You work across the stack: LLM integration, backend services, data pipelines, and enough frontend to ship end to end. Agent systems do not fit neatly into service boundaries, and your ability to work across all of them is a practical requirement.
Communication
You explain technical decisions clearly to both engineers and the product and design people you work with day to day. You document your designs, write pull requests that tell a story, and give direct feedback without being abrasive.

Benefits

Comp & perks

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
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!

ATS Keywords

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Hard Skills & Tools

AI Agent EngineeringContext ManagementModel SelectionCost EngineeringEvaluation FrameworksFull-Stack DeliveryRetrieval StrategiesConversation MemorySummarisationToken Economics

Soft Skills

Product ThinkingCommunicationTechnical JudgementCode ReviewMentoringCollaborationProblem SolvingFeedbackInfluencingDesign Documentation