Want to build AI agents that actually give trustworthy, explainable answers not confident guesses? This is a role for a GenAI engineer who knows that the way you get there is by grounding LLMs in real, governed enterprise knowledge.
You’ll join a specialist team at a global life-sciences organisation building a new generation of knowledge-graph-powered AI agents. Your focus is the tooling and the applications on top of the intelligence layer, not the underlying data.
What you will build:
Picture an agent that answers a question like “which priority hospitals in the US have decreasing sales?” To do it, the agent reads the definitions of the business terms from a knowledge graph so it understands the question, then queries the data warehouse for the real figures, and composes a grounded, traceable answer.
That grounding is the whole point: it’s what cuts hallucination and makes every answer explainable. If building that kind of system sounds like your idea of a good problem, read on.
What you will do:
Design and build LLM-powered agents and retrieval solutions on top of enterprise knowledge and data
Connect agents to enterprise systems through tool definitions and MCP-style connections
Benchmark and evaluate models, then take solutions from prototype into production
Build reusable frameworks and accelerators for agentic AI
Define the testing, evaluation, monitoring and governance for what you ship
What you will bring (essential):
Strong hands-on GenAI / LLM engineering – you’ve built real solutions with LLMs: agents, RAG, prompt and tool design, benchmarking, and shipping to production
Hands-on experience with knowledge graphs and semantic web in applications – SPARQL, RDF and related standards
A clear communicator who works well with both technical and business stakeholders
4+ years of AI engineering experience is a starting point — we care far more about genuine depth building LLM-powered systems than years on paper.
Nice to have:
MCP (very learnable if you know LLMs and Python)
Vector databases, embeddings and semantic search
Any graph or semantic tooling — Neo4j, Stardog, metaphactory, Snowflake and similar (current set up is standards-based and vendor-neutral, so the standards matter more than any one product)
Life sciences, pharma or other regulated-industry experience
The details:
Contract role
EU-remote based
Occasional on-site workshops in Germany (roughly every couple of months)
Start: 1st October
Runs to year-end initially, with strong potential to extend into a full project in the new year
Apply Now
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Barcelona, Catalonia, Spain (Remote) 