Senior Generative AI Engineer

About the role

About Motorway Motorway is the UK's largest online car-selling platform. We connect private sellers directly with over 8,000 dealers nationwide. Our online platform helps sellers achieve great prices for their cars while giving dealers fast, reliable access to the stock they want for their dealerships. Founded in 2017, our award-winning, technology-led approach has redefined the experience of selling a car. Motorway is backed by some of the world’s leading technology investors, having raised £143 million in Series C funding. This is a unique opportunity to join a fast-growing scale-up at a crucial phase of growth and help change an industry for the better. About the team GenAI Engineering sits within Motorway's Data and AI function, alongside Machine Learning and the Machine Learning Data Platform. We own the AI features that shape how buyers and sellers experience the marketplace, from agentic workflows in the customer journey to LLM-powered tooling for our dealer network. What we build goes into the hands of real sellers and thousands of verified dealers, usually within weeks. When it works, someone sells their car more easily. When it doesn't, we hear about it. That feedback loop is short, and it shapes how we work. We have a track record of successful deployments and a strong reputation as a result. About the role We're looking for a Senior GenAI Engineer to build AI applications and agentic workflows for Motorway, taking them from rapid prototype through to production-grade systems that deliver measurable value. You'll take a problem that isn't fully defined yet and see it through to a reliable system running in production: the prompting, the retrieval, the agent design, the evals, the guardrails, the monitoring. You own that whole stack. There's no separate team who "productionises" your work later. Most of what makes a GenAI feature good sits around the model rather than in it. The prompting is rarely the hard part. Retrieval quality is, and so is the data feeding it, evaluation you can trust, sensible behaviour when things fail, and getting it live and keeping it there. If that's the work you want to do, we're not precious about the route you took to get here. Some of us came through data science and machine learning and deliberately built up our software engineering; others came the other way. In this role, you will: Own AI features end to end, from an ambiguous problem through to a reliable, observable system that real customers depend on. Build the retrieval and data foundations these features stand on, and treat retrieval quality as a first-class engineering problem rather than a tuning exercise. Design evaluation and monitoring approaches that make quality, reliability and safety measurable rather than assumed. Build AI applications and agents using LLMs, orchestration, APIs and data systems, applying solid production software practices throughout. Make the calls on models, cost and latency that shape what a feature costs us to run, and document the reasoning so others can follow it. Prototype quickly, work out what is reusable, and harden the patterns that work into components the rest of the team adopts. Raise the quality of the work around you through code review, design feedback and the standards you set by example. Represent your technical decisions directly to product managers and stakeholders, translating model behaviour into business consequence. About you You've shipped AI or ML systems that real users depend on, and you've stayed close enough to production to know how they behave when they break. Strong Python, and SQL you genuinely use. A lot of this work is data work: assembling grounding data, building golden datasets, and digging into why something failed. You've invested deliberately in your software engineering craft: testing, error handling, CI/CD, observability, and the MLOps practices that keep things healthy once they're live. You're hands-on across the GenAI stack: LLM APIs, retriev

Skills