Knowledge Engineer

Project Description / Key Responsibilities

• Own ontology and semantic strategy across C4AD engagements, setting the approach, standards, and quality bar for knowledge engineering across the practice.
• Assure semantic layers that ground enterprise AI, reviewing, governing, and signing off on the ontological structures that AI systems in our programmes reason over.
• Design, develop, and govern enterprise ontologies, taxonomies, and knowledge graphs that accurately represent business domains, their relationships, and the rules that govern them.
• Build and govern semantic models using RDF/OWL, SHACL, and SPARQL, and property graph approaches using platforms such as Neo4j, Amazon Neptune, or Stardog.
• Apply and adapt industry-standard ontologies (e.g. FIBO, BIAN, GS1, TM Forum) to meet organisation-specific semantic requirements.
• Develop and enforce semantic modelling standards, naming conventions, versioning, and curation workflows, and drive adoption of those standards across delivery teams.
• Enable AI and ML applications through knowledge graph enrichment, entity resolution, semantic search, and ontology-powered RAG pipelines.
• Engage business SMEs and domain experts to elicit and formalise implicit business rules, relationships, and concepts into explicit ontological structures.

Key Skills

• 5+ years of experience in knowledge engineering, ontology design, or semantic modelling, with a track record of owning the knowledge engineering approach on complex programmes, not just contributing to it.
• Proven deep expertise in ontology engineering, authoring OWL ontologies, RDFS schemas, SHACL constraint shapes, and SPARQL queries, with real enterprise delivery behind it.
• Hands-on experience with graph databases and knowledge graph platforms such as Neo4j, Amazon Neptune, Stardog, or GraphDB.
• Proficiency with semantic tooling including Protégé, TopBraid Composer, or equivalent ontology authoring environments.
• Strong understanding of how semantic models connect to downstream data products, metadata catalogues, and AI/ML enrichment workflows, and the ability to assure that integration is sound.
• Knowledge of property graph modelling and the ability to move between RDF-based and property graph paradigms depending on platform and use case.
• Experience with entity resolution, semantic search, and knowledge-driven RAG architectures.
• Experience working within modern data architectures including data mesh, data lakehouse, and federated governance models.
• Background in linked data principles (W3C standards) and LLM grounding techniques, including GraphRAG and ontology-grounded generation, and practical experience applying them in enterprise contexts.
• Industry experience in Financial Services, Government, Health, Energy, or Utilities sectors in APAC is highly regarded.
• Good communication skills with the ability to engage both technical teams and business SMEs.

Benefits of working at Accenture

• · 18 weeks paid parental leave
• · Long & short-term career break opportunities
• · Structured career development program
• · Local and international career opportunities.
• · Certified as a Family Inclusive Workplace™
• · Flexible Work Arrangements - centered around Accenture’s Truly Human ethos and our commitment to supporting the health and wellbeing of our people.
• · We are proud to be in the top 3 of last year’s Diversity & Inclusion Index!

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