Software Engineer, Personio

• 4+ years building and shipping production software, working independently
• Strong Python skills — our primary backend language — across APIs, services, and data integration
• Hands-on experience building production AI products: agentic workflows and RAG pipelines with LLM APIs, agent frameworks (e.g. LangGraph), and MCP
• Judgement about where AI adds value and where it's overkill
• A track record of production-grade work: testing, observability, and secure handling of auth and data
• Experience deploying and running services in the cloud (AWS, Docker, CI/CD)

Desirable

• The Internal AI team works as a product team for Personio's own operations: embedding directly in business functions, building the agents and automations that make change real, and staying accountable for outcomes
• At the core of that mission is Personio's internal AI operating system: the layer of agents, automations, and tools each function runs on
• The Software Engineer (d/f/m) - Internal AI is the technical engine of that mission
• You design, build, and maintain the internal products and tools the rest of the company runs on: complex agents, cross-functional automations, and the applications around them
• You own software end-to-end, from architecture through to production and ongoing maintenance
• This role sits where strong software engineering meets applied AI: you bring the engineering depth to make the hardest problems solvable and the judgment to build the right thing
• Build and ship internal products and tools
• Design, build, and ship Personio's internal products and tools end-to-end: complex agents, cross-functional automations, and the applications and interfaces around them
• Make the architecture calls: choose the right patterns, manage complexity, and make pragmatic trade-offs between reliability, cost, and delivery speed
• Productionise and maintain
• Define what "done" means for an internal AI product: secure auth, error handling, monitoring, rollback, and documentation, then build to that bar
• Own systems over their full lifetime: maintain, monitor quality with Personio's eval platform, catch degradation, and keep them reliable long after launch
• Contribute to the AI platform
• Build on and extend Personio's internal AI platform: agent orchestration, the eval platform, context layer, and connector library
• Feed production experience back into the platform: the use cases you build surface the requirements, and you collaborate with AI Platform engineers to evolve shared infrastructure and the skills library so what you build compounds across the team
• Engineering standards
• Contribute to Personio's internal AI engineering standards: what production-grade solutions look like, how they are evaluated, deployed, and maintained
• Document solutions clearly so they can be maintained, extended, and handed over without you in the room