Working Student: AI Engineer, Agentic Systems (m/f/d)

• Design and build agentic systems: multi-step reasoning, tool calling, MCP, memory, retrieval, structured outputs
• Run the research loop: read what is current, form a hypothesis, build the experiment, measure, then decide. Kill your own ideas when the numbers say so
• Evaluate rigorously: eval datasets, offline and online scoring, LLM-as-judge, A/B tests, tracing and dashboards. We ship on evidence, not vibes
• Build the backend around the agents: Python services and APIs, streaming interfaces, database and schema work
• Own the DevOps: containerize, deploy on GCP (Cloud Run, CI/CD), instrument logs, metrics, traces, and alerts, then debug your own production incidents
• Scale what you ship: latency, cost per conversation, model routing and fallbacks across providers, graceful failure when a model or a vendor misbehaves
• Take features from prototype to production traffic end to end, with the AI engineering team

Requirements

• Enrolled student in Computer Science, Data Science, or a related field
• Strong Python. TypeScript is a plus
• Research-driven: you read papers, benchmarks, and model cards critically, and you can tell a real result from a cherry-picked one. You reach for an experiment before an opinion
• You have built agents yourself (side project, hackathon, research, internship) and can explain the design decisions
• Current on how agentic systems work today: tool calling, MCP, context engineering, orchestration patterns, eval practice, and where each one breaks
• Solid backend fundamentals: HTTP APIs, async, databases, version control, testing
• Comfortable in the cloud: containers, deployments, logs, metrics, traces. GCP experience is a plus
• You do the unglamorous parts too. A flaky deployment pipeline and a slow query are part of making an agent work
• You know that an agent working once on your machine is 20% of the job
• Fluent in English

Bonus: LangGraph or LangChain experience (any orchestration framework counts, and none is fine if you can reason about agent design); observability and eval tooling such as Langfuse, LangSmith, or Braintrust; streaming and real-time systems (WebSockets, gRPC); infrastructure as code and CI/CD; LLM cost and rate-limit work; published research or a thesis in ML or NLP.

Benefits

• Competitive compensation above market rate
• Public transportation benefits
• Flexible scheduling around your studies
• Real ownership from day one: you deploy to production, not to a sandbox
• Direct impact: your work reaches Fortune 500 clients within weeks
• Freedom to experiment with frontier models and tooling, with a budget behind it
• An engineering environment built to a compliance standard: GDPR and DSGVO compliant, EU AI Act aligned, ISO 27001 certified, EU data residency on GCP
• Potential extension beyond the initial engagement

Send your CV and a link to an agent you built. Tell us what broke in production, how you found out, and what you measured to confirm the fix.

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Quelle: Statistisches Bundesamt (Destatis), Tabelle; dl-de/by-2-0; eigene Berechnung