{"id":1253300,"url":"https://alion.io/job/astar-research-engineer-social-cognitive-computing-iaic","title":"Research Engineer, Social & Cognitive Computing, IAIC","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":75,"open_postings":35,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-13T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"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":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"GraphDB","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"LLM","optional":false},{"name":"Neo4j","optional":false},{"name":"NLP","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"Qwen","optional":false},{"name":"RAG","optional":true}],"status":"live","first_seen_at":"2026-09-25T18:38:00Z","employer_posted_date":"2026-09-25","last_verified_at":"2026-10-12T19:29:03Z","board_verified":true,"closed_at":null,"days_open":17,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":17},"description":"Job Summary\n\nJob Purpose\nThe Knowledge Engineer will be responsible for designing, constructing, and maintaining knowledge graphs derived from heterogeneous enterprise documents. The role involves leveraging Large Language Models (LLMs) for knowledge extraction, ontology-driven structuring, and question answering over knowledge graphs to support intelligent search, reasoning, and decision-making systems.\nKey Responsibilities\nLeverage LLMs to extract structured knowledge (entities, relations, attributes) from unstructured and semi-structured documents such as:Maintenance manuals\nProduct design documents\nTroubleshooting guides\nLessons learned reports\n\nProcess documents in multiple formats, including Word, PDF, PowerPoint, and Excel.\nDesign, construct, and maintain knowledge graphs, including:Schema and ontology design\nEntity and relationship modelling\nData normalisation and validation\n\nImplement and manage knowledge graph storage using graph databases such as Neo4j and GraphDB.\nDevelop LLM-powered question answering and interaction pipelines over knowledge graphs, enabling:Natural language queries\nContext-aware and explainable responses grounded in structured knowledge\n\nCollaborate with domain experts and stakeholders to refine ontologies, extraction logic, and use cases.\nRequired Skills & Experience\nStrong understanding of ontology design, knowledge graph modeling, and semantic data representation.\nProficiency in Python, with experience building data processing and NLP pipelines.\nSolid understanding of prompt engineering and LLM-based workflows.\nHands-on experience working with LLMs, including both:Closed-source models (e.g., GPT-based APIs)\nOpen-source models (e.g., LLaMA, Qwen, etc.)\n\nExperience with graph databases, particularly Neo4j and/or GraphDB.\nPreferred / Advantageous Skills\nExperience in information extraction, document understanding, or NLP pipelines.\nFamiliarity with RDF, OWL, SPARQL, or Cypher is a plus.\nExperience integrating LLMs with structured knowledge sources (e.g., KG-augmented QA, RAG with graphs).\nKey Competencies & Attributes\nStrong analytical and problem-solving skills, with the ability to translate unstructured information into well-defined data models.\nAbility to work independently while collaborating effectively within a multidisciplinary team.\nGood communication skills to engage with technical and non-technical stakeholders.\nDemonstrated willingness to continuously learn and keep up with advances in LLMs, NLP, and knowledge 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