{"id":2649068,"url":"https://alion.io/job/agency-for-science-technology-and-research-knowledge-graph-engineer-senior-engineer","title":"Knowledge Graph Engineer / Senior Engineer","company":{"id":50235,"name":"Agency for Science, Technology and Research","domain":"a-star.edu.sg","url":"https://alion.io/company/a-star-edu","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":{"grade":"B","score":80,"open_postings":46,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":21,"computed_at":"2026-10-21T05:46:15Z"}},"role":"Industrial Engineering","role_family":"Industrial Engineering","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Singapore"],"countries":["SG"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":39000,"max_usd":78000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":358},"experience_years_min":1,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Amazon Neptune","optional":false},{"name":"CI/CD","optional":false},{"name":"Computer Vision","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"GraphQL","optional":false},{"name":"Hugging Face","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM","optional":false},{"name":"Neo4j","optional":false},{"name":"NLP","optional":false},{"name":"Prefect","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"spaCy","optional":false},{"name":"Transformers","optional":false},{"name":"Digital Twin","optional":true}],"status":"live","first_seen_at":"2026-10-15T21:06:35Z","employer_posted_date":"2026-10-15","last_verified_at":"2026-10-20T17:56:14Z","board_verified":true,"closed_at":null,"days_open":5,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":5},"description":"Job Description\nKnowledge Graph Engineer / Senior Engineer\n(Group: Sustainable Systems and Devices)\nAbout the role\nWe 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.\nYou 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.\nRoles and Responsibilities\nDesign 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\nBuild 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\nImplement and operate NLP pipelines for entity extraction, relation extraction, and document parsing, turning industrial technical text into graph-ready triples at scale\nDesign and expose graph knowledge through well-structured APIs consumed by robotics execution, and decision-support modules\nTranslate research prototypes (new ontology modules, reasoning rules, embedding models) into production-grade code with appropriate testing, monitoring, and documentation\nEstablish data quality standards, entity resolution processes, and provenance tracking to ensure the graph remains trustworthy as it scales across data sources and domains\nSupport prototype development, testing, and validation of knowledge-driven automation solutions in lab and production environments\nPrepare technical documentation, reports, and presentations for internal and external stakeholders\nJob Requirements\nTechnical Skills\nStrong foundation in graph database systems and query languages, including proficiency in Cypher (Neo4j) and/or SPARQL (Stardog)\nSolid software engineering practices including Git/version control, modular code development, API design, and CI/CD pipelines\nProficiency in Python, with experience in data pipeline frameworks such as Apache Airflow, Prefect, or equivalent\nHands-on experience with NLP tooling for information extraction (e.g., spaCy, Hugging Face Transformers, or equivalent)\nFamiliarity with ontology standards (OWL, RDF, SKOS) sufficient to implement and extend schemas designed by a KG Scientist\nProficiency in one or more of the following areas:Graph database administration and performance optimisation (Neo4j, Amazon Neptune, Apache Jena, or Stardog)\nREST or GraphQL API design for knowledge and data services\nLLM integration: retrieval-augmented generation (RAG) pipelines or LLM-based information extraction\nEntity resolution and record linkage across heterogeneous data sources\n\nBonus Skills (Preferred but not required)\nExposure to manufacturing or industrial data formats (aerospace Quality Management System ISO standards, Bills of Materials, inspection schemas)\nExperience with digital twin platforms or industrial IoT data integration\nQualifications\nBachelor's or Master's degree in Computer Science, Data Engineering, Information Systems, or a related field\nAt least 1 year of relevant experience building and operating production graph 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