Research Engineer, Social & Cognitive Computing, IAIC

A*STAR - Agency for Science, Technology and Research; Singapore; On-site; SGD 60,000 - 90,000; Full time

Job summary

A*STAR - Agency for Science, Technology and Research in Singapore is seeking a Knowledge Engineer. The role involves designing and maintaining knowledge graphs using LLMs and requires strong skills in ontology design, Python, and NLP workflows.

Ideal candidates will work with various document types and collaborate with stakeholders to enhance knowledge extraction processes. If you have experience with graph databases and a passion for knowledge engineering, we want to hear from you.

Qualifications

• Strong understanding of ontology design and knowledge graph modelling.
• Proficiency in Python for data processing and NLP.
• Experience working with both closed-source and open-source LLMs.

Responsibilities

• Leverage LLMs to extract structured knowledge from unstructured documents.
• Design, construct, and maintain knowledge graphs.
• Implement knowledge graph storage using graph databases.

Skills

Ontology design

Knowledge graph modelling

Semantic data representation

Python

NLP pipelines

Prompt engineering

Graph databases (Neo4j, GraphDB)

Tools

Neo4j

GraphDB

Job description

Job Purpose

The 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.

Key Responsibilities

• Leverage LLMs to extract structured knowledge (entities, relations, attributes) from unstructured and semi‑structured documents such as:
• Maintenance manuals
• Product design documents
• Troubleshooting guides
• Lessons learned reports
• Process documents in multiple formats, including Word, PDF, PowerPoint, and Excel.
• Design, construct, and maintain knowledge graphs, including:
• Schema and ontology design
• Entity and relationship modelling
• Data normalisation and validation
• Implement and manage knowledge graph storage using graph databases such as Neo4j and GraphDB.
• Develop LLM‑powered question answering and interaction pipelines over knowledge graphs, enabling:
• Natural language queries
• Context‑aware and explainable responses grounded in structured knowledge
• Collaborate with domain experts and stakeholders to refine ontologies, extraction logic, and use cases.

Required Skills & Experience

• Strong understanding of ontology design, knowledge graph modelling, and semantic data representation.
• Proficiency in Python, with experience building data processing and NLP pipelines.
• Solid understanding of prompt engineering and LLM‑based workflows.
• Hands‑on experience working with LLMs, including both:
• Closed‑source models (e.g., GPT‑based APIs)
• Open‑source models (e.g., LLaMA, Qwen, etc.)
• Experience with graph databases, particularly Neo4j and/or GraphDB.

Preferred / Advantageous Skills

• Experience in information extraction, document understanding, or NLP pipelines.
• Familiarity with RDF, OWL, SPARQL, or Cypher is a plus.
• Experience integrating LLMs with structured knowledge sources (e.g., KG‑augmented QA, RAG with graphs).

Key Competencies & Attributes

• Strong analytical and problem‑solving skills, with the ability to translate unstructured information into well‑defined data models.
• Ability to work independently while collaborating effectively within a multidisciplinary team.
• Good communication skills to engage with technical and non‑technical stakeholders.
• Demonstrated willingness to continuously learn and keep up with advances in LLMs, NLP, and knowledge engineering.