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 general 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
- Strong Python and modern API development
- Solid software engineering fundamentals — Git, CI/CD, testing, cloud-native architecture
- A clear communicator who works well with both technical and business stakeholders

4+ years of AI engineering experience is a

📌 AGENTIC AI ENGINEER (Barcelona)
🏢 Data & AI Magazine
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