(Senior) AI Platform Engineer (m/f/d)

Chrono24

1d•Onsite

About The Position

As an AI Platform Engineer, you will empower Chrono24's engineers to work with AI. This is a newly created role in our Platform Engineering team, reporting directly to the Director Platform Engineering. Your mission is to make AI a reliable part of how our engineers build software, ensuring systematic, reproducible engineering they can count on every day. You will work hands-on and close to the teams, on a platform trusted by millions of watch enthusiasts worldwide. This role offers real ownership, direct visibility, and the freedom to shape how a fast-growing engineering organization adopts AI.

Requirements

A strong software engineering background with substantial development experience.
Roughly eight to ten years of development experience (useful reference, not a hard requirement).
A genuine passion for AI, with hands-on experience and a deep understanding of how to use it in software development.
The drive to keep up as the field moves fast.
Language- and stack-agnostic: the specific language or stack does not matter.
Being smart and quick to understand how the team works.
Very good English skills.

Nice To Haves

German proficiency is a plus.

Responsibilities

Build the harness around our agents: the skills, tools, and structure that wrap a model so it can run autonomously.
Build quality automation and build processes that keep agent output reliable as AI raises the volume of code and change.
Implement AI code review plus automated checks like tests, linters, and static analysis, running on every merge request, so quality holds even though the model underneath is non-deterministic.
Apply software engineering judgment to determine whether a skill or agent run actually produced what we want, holding agent output to a high bar and closing the loop when it falls short.
Make our foundations AI-friendly: improve documentation, code structure, and the rules and conventions agents work from, plus the guardrails that let them work well and safely.
Evaluate AI tooling pragmatically and guide build-versus-buy decisions, so teams adopt what genuinely helps and skip what only adds noise.
Embed with engineering teams to understand their real problems, build solutions with them, and roll out what works so good practice spreads across teams instead of staying in one corner.