AI / LLM Engineer – Werkstudent
Posted 97ds ago
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Job Description
Working Student in AI/LLM Engineering at CognitX AI GmbH, building privacy-first AI solutions for data analytics and process automation. Collaborate with senior AI experts on enterprise use cases.
Responsibilities:
- Build and improve agentic workflows (tool/function calling, planning, self-checks) for analytics, summaries, and visualizations, task automation.
- Implement adapters/tools to connect LLMs with internal and external services.
- Contribute to our FastAPI backend (clean interfaces, validation with Pydantic, tests).
- Develop evaluation metrics to measure accuracy, latency, and cost.
- Optimize prompts, retrieval/contexting, and execution strategies for privacy, reliability, and performance.
- Ship in containers (Docker) and collaborate on deploys (Kubernetes), CI, and observability.
- Document decisions and share learnings with the team.
Requirements:
- Experience with LLMs/AI agents (function/tool calling, RAG, MCP, or agent frameworks) and Machine Learning fundamentals.
- Strong Python skills for production code and data work (typing, tests, packaging).
- Familiarity with APIs and microservices (FastAPI preferred), Git, and containerized dev (Docker).
- Solid problem-solving, clear communication, and a proactive, ownership mindset.
- Currently enrolled in Computer Science, Data Science, AI, or a related field (eligible to work as a Werkstudent:in).
- Nice to have: Retrieval/RAG stacks (embeddings, chunking, evaluators), Redis, PostgreSQL.
- Kubernetes, GitLab CI, observability (logs/metrics/traces).
- React/TypeScript for light UI tooling.
- Interest in EU privacy/GDPR and safety-by-design for enterprise AI.
- Exposure to self-hosted/open models (e.g., Llama, Mistral etc) and model serving.
- Knowledge-graph concepts; graph queries.
Benefits:
- Work on meaningful, privacy-first AI with real enterprise pilots.
- Learn fast with tight mentorship from a team of Senior AI Experts.
- Flexible hours, remote-friendly, and impact from day one.
- Competitive Werkstudent compensation.







