AI Engineer

At hlpy, we’re building much more than a company. We’re building the technology platform that is transforming how mobility services are delivered across Europe. Every month, our platform orchestrates tens of thousands of real-world operations, connecting drivers, insurers, fleets, OEMs, service providers and business partners through technology. As we continue to expand internationally, broaden our product offering and integrate new businesses, the complexity of what we do grows with us. Scaling hlpy isn’t simply about increasing revenues. It’s about building an organization capable of executing consistently across countries, products and teams. To get there, strategy alone isn’t enough. That’s why we’re looking for a genuinely Senior AI Engineer to take ownership of it. This is the most senior hands-on AI role at hlpy, and it comes with a specific kind of mandate. What exists today is deliberately early: prototypes and first working versions, built at speed to prove that the ideas hold. What they were not yet built for is scale, and our evaluation coverage is thinner than we want it to be. It is an open brief. You would be arriving early enough to shape the foundations rather than inherit them finished. 60% building, running and proving out our production AI agents. ~25% making HLPY’s Engineering organisation faster by embedding AI properly into how we build software. ~15% being the company’s go-to person for what AI can — and can’t — do. We want to be upfront that this is not just a coding role : you’ll have a broad impact across the company, and helping the wider organisation make the most of AI is a core part of the job. Own the agent ecosystem, and prove it works Own the design, delivery, reliability and cost of our production agents end to end. Set the architectural direction: agent patterns, model selection, orchestration, and the trade-offs behind each choice. Build and maintain the APIs and MCP servers that connect our agents to the hlpy product and to third-party systems. Establish the evaluation practice properly: datasets built from thousands of real assistance cases, offline evaluation, regression gates before release, and per-country monitoring in production. Build the automated loop — a quality threshold is breached, an issue is created, an agent proposes an enhancement, it is tested, a human reviews and releases it — so that quality does not depend on anyone remembering to check. You will fix it. Put AI into how hlpy builds software Own our AI-assisted software development lifecycle. making it real is yours. Implement it, keep it current as the tooling changes underneath you, and roll it out across the tech organisation. Train and enable engineers to use it well — and be honest with them about where it does not help. Build the guardrails and documentation that make it safe to use at speed, with Security and Legal where customer data is involved. Treat this as engineering leverage, not advocacy. Done properly it makes every engineer here measurably faster, which is more impact than you will have with any single agent. Be the company’s AI reference point Be the person teams come to before they buy a tool or start an experiment. Build reusable blueprints and internal documentation so teams can build on your work without you in the room. Run occasional workshops and demos for non-technical teams. Make capabilities feel intuitive to colleagues who do not think in software, and measure yourself by whether they actually use what you built. Tell people plainly when AI is the wrong answer. This is most of the value of having an internal expert. Shape our AI usage guidelines with Security and Legal, so the company can move fast without exposing customer data or drifting into shadow AI. Not a research position. A meaningful share of your week goes on making other engineers better, and that is part of the role rather than a tax on it. If you scope it, you build it. Quality, reliability and data protection come first. Required Skills & Experience Have around 5+ years of professional software engineering experience, including meaningful time building LLM-based systems that real users depend on. Have taken an agent or LLM feature to production and then operated it — you know what breaks, and why. Treat evaluation as engineering rather than as a demo. You have built the datasets, harnesses and monitoring that tell you whether a change was an improvement, and you have an opinion about what makes them good. Design and consume APIs and integrations confidently and independently — REST, and ideally MCP. Are comfortable with the production basics: containerisation, CI/CD, observability, cloud. You find the problem that matters, scope it, propose the plan, and then deliver it without being managed through it. Have taken something from prototype to production-grade, and can talk about what had to change. You argue your case f