Knowledge Graph Engineer / Senior Engineer

Job Description

Knowledge Graph Engineer / Senior Engineer

(Group: Sustainable Systems and Devices)

About the role

We are seeking an experienced Knowledge Graph Engineer / Senior Engineer to design, build, and operate the knowledge graph infrastructure that underpins our remanufacturing platform. The role focuses on translating research-stage ontologies and reasoning prototypes into reliable, scalable systems that ingest real-world industrial data and serve structured knowledge to downstream applications.

You will work closely with the KG Scientist, and computer vision engineers to build end-to-end graph data pipelines, from EV battery disassembly logs to aerospace inspection records. This is a senior engineering role requiring both systems thinking and the ability to engage with cutting-edge graph and NLP tooling.

Roles and Responsibilities

• Design and maintain the graph database schema, storage architecture, and query layer; own performance, scalability, and reliability of the graph store as domain coverage grows across EV battery, aerospace, and manufacturing
• Build ETL/ELT data ingestion pipelines that bring structured and unstructured data (sensor logs, technical manuals, inspection reports, CAD metadata) into the knowledge graph, with validation and data quality checks
• Implement and operate NLP pipelines for entity extraction, relation extraction, and document parsing, turning industrial technical text into graph-ready triples at scale
• Design and expose graph knowledge through well-structured APIs consumed by robotics execution, and decision-support modules
• Translate research prototypes (new ontology modules, reasoning rules, embedding models) into production-grade code with appropriate testing, monitoring, and documentation
• Establish data quality standards, entity resolution processes, and provenance tracking to ensure the graph remains trustworthy as it scales across data sources and domains
• Support prototype development, testing, and validation of knowledge-driven automation solutions in lab and production environments
• Prepare technical documentation, reports, and presentations for internal and external stakeholders

Job Requirements

Technical Skills

• Strong foundation in graph database systems and query languages, including proficiency in Cypher (Neo4j) and/or SPARQL (Stardog)
• Solid software engineering practices including Git/version control, modular code development, API design, and CI/CD pipelines
• Proficiency in Python, with experience in data pipeline frameworks such as Apache Airflow, Prefect, or equivalent
• Hands-on experience with NLP tooling for information extraction (e.g., spaCy, Hugging Face Transformers, or equivalent)
• Familiarity with ontology standards (OWL, RDF, SKOS) sufficient to implement and extend schemas designed by a KG Scientist
• Proficiency in one or more of the following areas:
  • Graph database administration and performance optimisation (Neo4j, Amazon Neptune, Apache Jena, or Stardog)
  • REST or GraphQL API design for knowledge and data services
  • LLM integration: retrieval-augmented generation (RAG) pipelines or LLM-based information extraction
  • Entity resolution and record linkage across heterogeneous data sources

Bonus Skills (Preferred but not required)

• Exposure to manufacturing or industrial data formats (aerospace Quality Management System ISO standards, Bills of Materials, inspection schemas)
• Experience with digital twin platforms or industrial IoT data integration

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related field
• At least 1 year of relevant experience building and operating production graph database systems, ideally in a manufacturing, industrial, or research-adjacent environment