Key Skills

Job Description

The demo is the easy part. We build the AI that’s still running, and still useful, long after it. At Schuberg Philis, we see AI as an exciting and fast-evolving technology, but its real power lies in solving meaningful problems and creating lasting value. As an AI Python Engineer in our central team, you’ll turn that potential into dependable products and services. Working alongside talented engineers, you’ll build the software capabilities behind retrieval systems, agentic workflows, knowledge platforms and AI-powered applications designed to thrive long after the demo is over. You’ll also help identify where AI can create real value, and understand the problem deeply enough to build the right solution. Your work will be varied and impactful. One week, you might improve the quality of a Retrieval-Augmented Generation (RAG) platform using technologies such as OpenAI, Anthropic, LangGraph and vector search. The next, you could design Python services, build APIs, develop a knowledge assistant combining LLMs with Neo4j and GraphRAG, automate operational processes with agentic workflows, or shape the architecture of a new AI capability used across multiple teams. What you will work on Improving the quality of RAG platforms using OpenAI, Anthropic, LangGraph and vector search Designing Python services and building APIs Developing knowledge assistants with Neo4j and GraphRAG techniques Automating operational processes using agentic workflows Building intelligent search that helps engineers find answers across thousands of documents and systems Shaping the architecture of new AI capabilities used across multiple teams Here, building and running go hand in hand. The engineers making the decisions are also the engineers carrying the pager. That shared ownership makes operational excellence, observability and reliability every bit as important as elegant code, and you’ll own your solutions from design to production, learning from real-world feedback and continuously improving what you’ve built. You’ll join a team that celebrates engineering craftsmanship, autonomy and curiosity. We want you to challenge ideas, make bold but thoughtful decisions, and continuously improve both our technology and the way we work. We’d love to meet you if You’re first and foremost a software engineer who happens to be interested in AI. You enjoy understanding the problem before jumping to the solution, and you take pride in seeing your systems perform reliably in production. You have several years of solid experience building production-grade software with Python You’ve worked with Generative AI, LLMs, RAG, agentic workflows, LangGraph, LangChain, Neo4j, GraphRAG or semantic search, beyond side projects and demos You enjoy designing systems and taking responsibility for how they behave in production You naturally switch between architecture discussions and hands-on implementation You can turn ambiguity into a practical engineering approach You have strong opinions about software quality, testing and maintainability You’re interested in both technology and the business context it serves You like owning outcomes, not tickets, from idea to production and beyond You are currently based in the Netherlands. Questions? Please do not hesitate to contact Maggie: 0623757086 Only applications submitted through the website will be considered. Please note that we do not work with external recruitment agencies and we will not pay any fees for unrequested CV introductions.

Core Responsibilities

Build and operate production-grade AI software, including RAG platforms, Python services and APIs, knowledge assistants, intelligent search, and agentic workflows. Shape AI architecture and own solutions from design through production, with a focus on reliability, observability, and continuous improvement.

Requirements

Several years of experience building production-grade software with Python and hands-on experience with generative AI, LLMs, RAG, or related technologies beyond demos or side projects. Candidates should be strong software engineers who can design systems, implement them, make practical decisions amid ambiguity, and take ownership of production outcomes.