Machine Learning Engineer (m/f/d)

You’ll work as part of a team designing and building AI agents that run in real systems — not demos or research prototypes. The work spans the full lifecycle of these systems: data handling, model and agent design, orchestration, testing, deployment, and operation.
You’ll collaborate closely with other engineers and ML practitioners. This is a team environment where problems are discussed openly, ideas are shared, and solutions are built together. You’ll have space to experiment, but also responsibility for turning ideas into robust, maintainable systems.

There’s no rigid checklist. If you’re comfortable with Python and interested in building applied machine learning and agent-based systems, that’s a solid foundation. What matters most is curiosity, a willingness to dig into complex problems, and the ability to work well with others.
It’s helpful if you’re interested in how large language models behave inside agent setups — how prompting choices influence outcomes, how tool calls are triggered and handled, and how routing or decision logic shapes system behavior.
 
Experience that’s useful here

• Agent-based systems (LangGraph)
• Vector stores and retrieval systems (e.g. Cosmos DB)
• Working with foundation models and platforms such as Azure OpenAI, Gemini/Vertex AI
• Git and collaborative development workflows
• CI/CD pipelines and production deployments
• Cloud platforms, particularly Azure

Familiarity with concepts such as agent-to-agent communication (A2A) and the Model Context Protocol (MCP) is considered an advantage.

If you’ve worked on AI-related projects — professionally, at university, or on your own — feel free to share them. A GitHub repository is ideal, and a ZIP file works as well. Seeing how you approach problems is more important than perfectly polished material.