Applied AI Engineer

Applied AI Engineer – Agentic Systems | Remote (UK/Europe) | €100K–€150K + equity

You might call yourself an Applied AI Engineer, a Forward Deployed Engineer, a Product Engineer, or a backend engineer who ended up owning the LLM work. The title doesn’t matter. What matters is that you’ve built agents that real users or real business processes rely on, and you’ve done the hard work of making them reliable.

The company

A fast-growing, AI-first scale-up modernizing a huge, traditional UK market. It’s backed by top-tier VCs after a large recent raise, and already operating at real scale. Engineering is about 20 people working in squads of 2–3, so you’ll help set the patterns rather than inherit them. They’re hiring several engineers.

The work

AI agents already run core operations in production. You’ll bring these flows together, build the evaluation layer, and scale the system as the business grows.

You’ll decide where an LLM belongs, where plain code is better, and where a human needs to step in.

You’ll own reliability, latency, cost, monitoring and failure handling.

You’ll build shared agent tooling the rest of the team uses.

You’ll probably have

4+ years in software engineering with a strong backend focus (TypeScript/Node or Python; Postgres a plus)

Agents you’ve shipped and run in production: tool use, subagent orchestration, structured outputs, evals and feedback loops

Time at a product-led scale-up or AI-native company

A Computer Science or related degree

Things you build with AI outside work

Probably not for you if your focus is training or researching models, your AI work has mostly been client proofs of concept, or you mainly work on frontend.

The package: €100K–€150K base plus options. Fully remote across the UK and Europe, working UK hours. Visa sponsorship can be transferred if you’re already in London. The interview process takes about two weeks.

To apply: send your CV and 3–4 lines on an agent you’ve shipped. Tell us what it did, who used it, how you evaluated it, and one thing that broke and how you fixed it.