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 / Ventajas

- Flexible working hours
- Wellness allowance
- Mental health support
- Remote work from abroad policy
- Dental Benefits
- Life & Accident Insurance + Private Health Insurance

Responsabilidades

- Design and maintain high-speed GraphRAG ingestion pipelines converting ERP, SQL, unstructured ESG reports, and streaming data into labeled property graphs and RDF stores
- Build automated NER, entity linking, and deduplication workflows to unify vendor profiles, SKUs, and coordinates into canonical graph nodes
- Implement 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 agents
- Operationalize SHACL shapes into automated data quality tests in CI/CD to prevent data mutations in the graph
- Optimize multi-hop queries,



graph partitioning, and indexing for sub-second traversal over billions of nodes and edges
- Collaborate with AI/ML engineers to integrate graph databases with vector stores for hybrid search architectures
- Ensure alignment with enterprise OBDA approaches and scalable graph tooling

Requisitos principales

- Degree in Computer Science, Mathematics, Engineering, or a related technical discipline
- 4+ years of production experience with graph databases (Neo4j, Memgraph, TigerGraph) or RDF stores (GraphDB, Stardog, Virtuoso)
- Strong cloud experience (Azure preferred) and related tooling
- Advanced Python skills (RDFLib, NetworkX, PyGraphistry) for scalable data pipelines
- Experience with NLP frameworks (LangChain, LlamaIndex, spaCy) or LLM-based extraction
- Hands-on with data transformation tools (dbt) and vector stores (Qdrant, Pinecone, pgvector) for hybrid search
- Solid 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 plus
- Experience building MCP servers to expose graph tools to LLMs is a plus
- Experience with enterprise OBDA approaches at scale is a plus

- Collaborative
- Problem-solving
- Detail-oriented
- Graph databases (Neo4j, Memgraph, TigerGraph)
- RDF and SPARQL
- Cypher

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