As an AI Engineer (w/m/d) , you take ownership of the architecture and operation of our AI agents. You work closely with Data Scientists to turn AI prototypes into robust, reliable, and scalable production systems – and continuously evolve them.
Design, Build & Run: Architect reliable AI systems
Measure & Improve Quality: Define metrics, run systematic evaluations, and conduct A/B testing
Bridge the Gap: Work closely with Data Scientists to bring prototypes into production
Explore New Frontiers: Evaluate and apply the latest AI/ML technologies and practices
Apply software engineering best practices consistently – testing, clean code, and solid architecture.
Stay curious and actively explore new technologies, applying them where they create real value.
Work effectively in agile, cross-functional teams where fast delivery, learning, and iteration are the norm.
Use Python and AI frameworks such as Pydantic-AI , LangGraph, or similar to build robust solutions.
Use Docker and docker-compose to containerize applications.
Work confidently with Git as a core tool for team collaboration.
Bonus (nice to have): Bring experience with SQL, Spark, Kubernetes, Grafana, Prometheus, Graylog, Jenkins, or Java, and apply it to further improve our systems.
Ownership over tickets: You’re trusted with real responsibility, not just tasks. No unnecessary bureaucracy, no micromanagement – we rely on you to take things forward.
Build > Talk: We test what works – not what sounds good. Fail fast, learn faster.
High standards, low ego: We take our work seriously, but not ourselves. Direct feedback, honest collaboration, no drama.
Stay sharp: Hackathons, conferences, community – we invest in your growth and keep you at the cutting edge.
And yes – the fundamentals are covered too: 30 days of vacation + Dec 24 & 31 off, Smart Fridays (4 days week possible), mobility (Germany ticket & JobRad), sports & health offerings, mental health support, corporate benefits, RTL+ access, and more.
What matters more to us than your resume: a portfolio, a side project, a demo repo – anything that shows you don’t just talk about code, you ship it. Production-ready. Thought through. Done.
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