AI Engineer (m/f/d)

What this role is about AI has arrived in the German Mittelstand. The distance between a convincing demo and a system a department trusts every single day is still large — and it gets closed by work that almost nobody does properly: defining what "good" concretely means, building the system toward it, measuring whether it hits, and iterating until it holds. That is what you do: You don't start from zero. You usually get a real starting point, often a golden data set from the department of a German Mittelstand company: real cases, each with the result an experienced expert would deliver. Your job is to build a system that reaches that result reliably, to bring it into people's working day through an interface they can actually use, and to be able to prove that it works. Tasks You typically work on two to three customer projects in parallel. 80% Building: The core of the role Derive a defensible definition of "correct" from reference data, and build an eval that measures it Design and build the AI system itself: model and provider choice, context and prompt design, structured outputs, tool calling, agent/workflow logic, retrieval where it earns its place — and deterministic logic where an LLM isn't needed. Do error analysis and iterate deliberately instead of guessing — and demonstrate the improvement Build guardrails, escalation paths and monitoring, so the system doesn't fail loudly and confidently Build the production interface that goes with it: review screens, approvals, human-in-the-loop — in Next.js and TypeScript, not a throwaway prototype Roll the solution out and keep it running + improve it as AI advances 20% Communication: Mostly internal Capture and pass on your state in a structured way: what is built, what was measured, what is still open — in writing, without anyone having to ask Explain complex technical matters so that the project lead and colleagues without an AI background can decide on a sound basis Raise questions, blockers and wrong assumptions