(Senior) Knowledge Graph Engineer, ARTC

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 servicesLLM integration: retrieval-augmented generation (RAG) pipelines or LLM-based information extractionEntity 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

The above eligibility criteria are not exhaustive. A*STAR may include additional selection criteria based on its prevailing recruitment policies. These policies may be amended from time to time without notice. We regret that only shortlisted candidates will be notified.