Job Opportunities API

Job Description We are looking for a hands-on, production-focused Senior Knowledge Graph Engineer to join our growing AI Center of Excellence, responsible for using AI and machine learning to drive innovation across the organization. In this role, you will take the formal domain ontologies designed by our Knowledge Representation Architects and operationalize them into high-throughput, multi-hop systems. We welcome applications from European locations, with the possibility of remote work. Join us! Your work will directly power autonomous AI agents that solve complex sustainability challenges — including decarbonisation, sustainable procurement compliance, and supply chain resilience. You will bridge the gap between unstructured sustainability disclosures and structured graph databases, building entity-resolution pipelines that make enterprise data agent-ready. Your responsibilities will include (but will not be limited to): • Graph Infrastructure and Ingestion Pipelines • Design, implement, and maintain high-speed GraphRAG ingestion pipelines that transform relational data (ERP, SQL), unstructured ESG reports, and streaming feeds into operational Labeled Property Graphs (Neo4j, Memgraph) and RDF Triple Stores • A-Box Instantiation and Entity Resolution • Build automated Named Entity Recognition (NER), entity linking, and deduplication workflows to resolve mismatched vendor profiles, material SKUs, and facility coordinates into unified canonical graph nodes • Semantic Federation and External Data Integration • Implement automated ETL/ELT pipelines to map and federate internal supply chain data with external, open-source ontologies and registries (such as GLEIF for corporate ownership, W3C SSN/SOSA for IoT sensors, and Copernicus for geo-hazard alerts, PROV-O for data provenance) • GraphRAG and Agent Tooling • Partner with AI/ML Engineers to build low-latency GraphRAG retrieval layers—writing optimized Cypher and SPARQL queries, implementing NL2Query tools for agents, hybrid vector-graph indexing pipelines, and Model Context Protocol (MCP) tool endpoints for autonomous LLM agents • Deterministic Guardrails and Pipeline Validation • Operationalize SHACL (Shapes Constraint Language) shapes into automated data quality tests within CI/CD pipelines to prevent hallucinated or non-compliant data mutations from entering the enterprise knowledge graph • Performance Optimization and GraphOps • Optimize multi-hop query performance, graph partitioning, and database indexing strategies to handle sub-second traversal over billions of nodes and edges