Knowledge Graph Architect

Role OverviewWe are seeking an experienced Knowledge Graph Architect to design, build, and govern enterprise-scale Knowledge Graph (KG) solutions. This role requires deep hands-on expertise in KG modeling, ontology design, semantic technologies, and integration with modern AI systems such as LLMs and RAG pipelines. The ideal candidate will act as a technical leader, defining standards and guiding engineering teams to deliver robust, scalable graph-based solutions.Key ResponsibilitiesDesign and develop enterprise-scale Knowledge Graphs, with a strong preference for Neo4jLead KG modeling aligned with ontology principles, including:Entity and relationship designHierarchies, constraints, and semantic consistencyDefine and maintain ontologies using RDF/OWL and label-property graph modeling techniquesEstablish and enforce KG governance, including:Modeling standardsVersioning strategiesMetadata and documentation practicesArchitect KG integration with LLMs, including:RAG (Retrieval-Augmented Generation) pipelinesAI-powered enterprise applicationsCollaborate closely with product, data, and engineering teams to align KG solutions with business requirementsGuide and mentor engineering teams, perform model reviews, and ensure model consistency and qualitySupport KG lifecycle management, including evolution, optimization, and scalabilityRequired Skills & QualificationsStrong hands-on experience in Knowledge Graph modeling and architectureDeep understanding of ontology design and semantic modeling conceptsExpertise in RDF, OWL, and label-property graph approachesProven experience building and operating enterprise-scale KGsExperience with Neo4j or similar graph databasesStrong understanding of KG governance, versioning, and modeling standardsExperience integrating KGs with LLMs, RAG pipelines, and enterprise systemsAbility to review, validate, and optimize complex graph modelsStrong communication skills with the ability to guide and influence engineering teamsNice to HaveExperience with graph query languages (Cypher, SPARQL)Exposure to data ingestion pipelines and graph ETL processesBackground in AI/ML-driven knowledge systemsExperience working in large-scale enterprise environments

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