Senior Knowledge Graph Engineer

EcoVadis

23h•Hybrid

About The Position

EcoVadis is seeking a hands-on, production-focused Senior Knowledge Graph Engineer for its growing AI Center of Excellence. The role operationalizes formal domain ontologies into high-throughput, multi-hop systems that power autonomous AI agents addressing sustainability challenges such as decarbonisation, sustainable procurement compliance, and supply chain resilience. The engineer bridges unstructured sustainability disclosures and structured graph databases by building entity-resolution pipelines that make enterprise data ready for AI agents. The position involves building graph ingestion and retrieval infrastructure, integrating internal and external data, applying semantic web standards, and maintaining data quality and performance at scale. The role collaborates with AI/ML Engineers and Knowledge Representation Architects to deliver operational knowledge graph capabilities. The position is hybrid in Warsaw, with two office days per week, or fully remote from Poland. Applicants must be eligible to work and live in Poland.

Requirements

Degree in Computer Science, Mathematics, Engineering, or a related technical discipline.
At least 4 years of production experience building and querying graph databases, including Labeled Property Graphs or RDF Triple Stores.
Strong experience with cloud technology, preferably Azure and its ecosystem.
Advanced proficiency in Python, including RDFLib, NetworkX, or PyGraphistry, for scalable production-grade data pipelines.
Experience building entity-extraction pipelines using modern NLP frameworks such as LangChain, LlamaIndex, or spaCy, or LLM-based structured extraction.
Hands-on experience with modern data transformation tools such as dbt and integrating graph databases with vector stores such as Qdrant, Pinecone, or pgvector for hybrid search architectures.
Solid understanding of semantic web standards, including RDF, RDFS, OWL, SKOS, SHACL, RDF-star, and SPARQL; graph schema design principles including T-Box vs. A-Box separation; and mapping languages such as RML and R2RML.
Must be eligible to work and live in Poland.

Nice To Haves

Experience with domain-specific supply chain, carbon accounting (GHG Protocol), or lifecycle assessment (LCA) data structures.
Direct experience building Model Context Protocol (MCP) servers to expose graph tools to LLM agents.
Experience with enterprise OBDA approaches at scale.

Responsibilities

Design, implement, and maintain high-speed GraphRAG ingestion pipelines that transform relational data, unstructured ESG reports, and streaming feeds into operational Labeled Property Graphs and RDF Triple Stores.
Build automated named entity recognition, entity-linking, and deduplication workflows to resolve mismatched vendor profiles, material SKUs, and facility coordinates into canonical graph nodes.
Implement automated ETL/ELT pipelines to map and federate internal supply chain data with external ontologies and registries, including GLEIF, W3C SSN/SOSA, Copernicus, and PROV-O.
Partner with AI/ML Engineers to build low-latency GraphRAG retrieval layers, including optimized Cypher and SPARQL queries, NL2Query tools, hybrid vector-graph indexing pipelines, and MCP tool endpoints for autonomous LLM agents.
Operationalize SHACL shapes as automated data-quality tests in CI/CD pipelines to prevent hallucinated or non-compliant data mutations from entering the enterprise knowledge graph.
Optimize multi-hop query performance, graph partitioning, and database indexing to support sub-second traversal over billions of nodes and edges.