Knowledge Graph Architect

We 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.

• Design and develop enterprise-scale Knowledge Graphs , with a strong preference for Neo4j
• Lead KG modeling aligned with ontology principles , including:
• Entity and relationship design
• Hierarchies, constraints, and semantic consistency
• Define and maintain ontologies using RDF/OWL and label-property graph modeling techniques
• Establish and enforce KG governance , including:
• Modeling standards
• Versioning strategies
• Metadata and documentation practices
• Architect KG integration with LLMs , including:
• RAG (Retrieval-Augmented Generation) pipelines
• AI-powered enterprise applications
• Collaborate closely with product, data, and engineering teams to align KG solutions with business requirements
• Guide and mentor engineering teams, perform model reviews , and ensure model consistency and quality
• Support KG lifecycle management, including evolution, optimization, and scalability

• Strong hands-on experience in Knowledge Graph modeling and architecture
• Deep understanding of ontology design and semantic modeling concepts
• Expertise in RDF, OWL , and label-property graph approaches
• Proven experience building and operating enterprise-scale KGs
• Experience with Neo4j or similar graph databases
• Strong understanding of KG governance, versioning, and modeling standards
• Experience integrating KGs with LLMs, RAG pipelines, and enterprise systems
• Ability to review, validate, and optimize complex graph models
• Strong communication skills with the ability to guide and influence engineering teams

• Experience with graph query languages (Cypher, SPARQL)
• Exposure to data ingestion pipelines and graph ETL processes
• Background in AI/ML-driven knowledge systems
• Experience working in large-scale enterprise environments