Overview In this role you will operationalize domain ontologies into high-throughput graph systems powering autonomous AI agents solving sustainability challenges. You’ll bridge unstructured disclosures with structured graphs and build scalable ingestion and entity-resolution pipelines. You’ll enable semantic federation with external data and optimize performance for sub-second traversals at scale. You work closely with AI/ML engineers to drive practical, data-driven sustainability solutions.

Compensaciones / BeneficiosFlexible working hoursWellness allowanceMental health supportRemote work from abroad policyDental BenefitsLife & Accident Insurance + Private Health Insurance ResponsabilidadesDesign and maintain high-speed GraphRAG ingestion pipelines converting ERP, SQL, unstructured ESG reports, and streaming data into labeled property graphs and RDF storesBuild automated NER, entity linking, and deduplication workflows to unify vendor profiles, SKUs, and coordinates into canonical graph nodesImplement automated ETL/ELT pipelines to federate internal supply chain data with external ontologies and registries (GLEIF, W3C SSN/SOSA, Copernicus, PROV-O)Develop low-latency GraphRAG retrieval layers, write Cypher and SPARQL queries,



and create NL2Query tools for autonomous LLM agentsOperationalize SHACL shapes into automated data quality tests in CI/CD to prevent data mutations in the graphOptimize multi-hop queries, graph partitioning, and indexing for sub-second traversal over billions of nodes and edgesCollaborate with AI/ML engineers to integrate graph databases with vector stores for hybrid search architecturesEnsure alignment with enterprise OBDA approaches and scalable graph tooling Requisitos principalesDegree in Computer Science, Mathematics, Engineering, or a related technical discipline4+ years of production experience with graph databases (Neo4j, Memgraph, TigerGraph) or RDF stores (GraphDB, Stardog, Virtuoso)Strong cloud experience (Azure preferred) and related toolingAdvanced Python skills (RDFLib, NetworkX, PyGraphistry) for scalable data pipelinesExperience with NLP frameworks (LangChain, LlamaIndex, spaCy) or LLM-based extractionHands-on with data transformation tools (dbt) and vector stores (Qdrant, Pinecone, pgvector) for hybrid searchSolid understanding of semantic web standards (RDF, RDFS, OWL, SKOS, SHACL, RDF-star, SPARQL) and mapping (RML, R2RML)Experience with domain data in supply chain, carbon accounting (GHG Protocol), or LCA is a plusExperience building MCP servers to expose graph tools to LLMs is a plusExperience with enterprise OBDA approaches at scale is a plusCollaborativeProblem-solvingDetail-orientedGraph databases (Neo4j, Memgraph, TigerGraph)RDF and SPARQLCypher

📌 Senior Knowledge Graph Engineer (Barcelona)
🏢 Jobrapido
📍 Barcelona