Agentic AI Engineer (Life Sciences & Knowledge Graphs) Contract · EU-Remote 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. Youll 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 youll 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: its 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 youll 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 youll bring (essential) * Strong hands-on GenAI / LLM engineering - youve 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 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 Please hit the apply button if you are Interested

📌 Agentic AI Engineer (Life Sciences Knowledge Graphs) (Madrid)
🏢 Data Science Talent
📍 Madrid