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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and operating AI agent platforms, particularly with PydanticAI, LiteLLM, and Langfuse, while ensuring compliance with regulatory requirements in banking, insurance, and pharmaceuticals. Proficient in developing RAG pipelines and managing multi-tenant architectures with a focus on performance metrics and auditability.
Highest-signal resume keywords
Production Software Engineering ExperiencePython ProgrammingLLM Experience in ProductionRAG Systems DevelopmentRegulatory Compliance Knowledge
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonRAG SystemsLLMPydanticAILiteLLMLangfusePgvectorKubernetesRetrieval TuningRe-Ranking
Soft Skills
Excellent German SkillsGood English Skills
Tools & Technologies
LangGraphMCP Tools
Industry Keywords
EU AI ActDORABaFinMaRiskGxP
Tech Stack
Tools & technologiesKubernetesPython
About the role
Key responsibilities & impact- Own the agent architecture and core AI platform
- Build a production-ready AI agent platform based on PydanticAI, LiteLLM, and Langfuse
- Develop RAG pipelines with pgvector at scale, including multi-tenant isolation, index rebuild strategies, hybrid search, and re-ranking
- Build a multi-tenant token quota service and tenant-specific evaluation frameworks
- Integrate MCP tools as a core part of the architecture
- Ensure end-to-end auditability from prompt to output
- Make architectural decisions for banking, insurance, and pharmaceutical clients
- Document relevant architectural decisions in ADRs
- Provide visibility into LLM calls, costs, and latency in Langfuse
- Report to Christoph Knöll (Founder/CEO) and work closely with the Head of AI
- Describe an LLM or RAG system you have brought into production as part of the application process
Requirements
What you’ll need- 5+ years of production software engineering experience with Python
- Experience with LLMs in production, including tokens, latency, costs, and evaluations
- Hands-on experience building and operating RAG systems, including retrieval tuning and re-ranking
- Experience using an agent framework such as LangGraph, PydanticAI, or a comparable framework in production
- Excellent German and good English skills
- Experience sizing pgvector for large-scale data volumes
- Foundational knowledge of Kubernetes
- Experience with regulatory requirements such as the EU AI Act, DORA, BaFin/MaRisk, or GxP
- Experience with LiteLLM, Langfuse, or MCP is a plus but not required
- Based in Germany
Benefits
Comp & perks- Permanent employment contract
- 40 hours per week
- 28 days of annual leave
- Fully remote, with residence in Germany
- Flexible working hours
- Team days at the Munich office approximately once a month
- Modern technology stack and dedicated time to evaluate new models, frameworks, and retrieval approaches
- Conferences and training
- Opportunities to represent the company externally as an expert, for example through talks or technical articles