Graph Database Architect

Responsibilities:

• Design and build the production knowledge graph platform: data models, ontology standards, and infrastructure.
• Build knowledge graphs representing educational content, skills taxonomies, learner models, and curriculum structures.
• Enable GraphRAG architectures integrating knowledge graphs with LLM retrieval pipelines for contextual AI responses.
• Define ontology governance standards, schema versioning, and knowledge base quality assurance processes.
• Optimize graph database performance: query tuning, index design, partitioning, and scaling strategies.
• Build entity resolution and knowledge base construction pipelines from heterogeneous data sources.
• Design semantic search capabilities combining graph-native and vector-based hybrid retrieval approaches.
• Collaborate with ML teams on graph-native features: recommendation, adaptive learning paths, and gap detection.
• Evaluate and evolve the graph technology stack as platform and data complexity scales.
• Define and enforce data quality standards for the knowledge graph: completeness checks, consistency validation, and remediation workflows.
• Drive graph-based data integration across heterogeneous sources and establish reasoning pipelines for semantic AI applications.

Requirements:

• Strong experience in designing and managing graph database solutions in production environments.
• Deep understanding of knowledge graphs, ontologies, and graph data modeling.
• Hands-on experience with Neo4j, JanusGraph, Apache Jena, or similar graph databases.
• Strong knowledge of RDF, property graphs, SPARQL, and Cypher query languages.
• Experience building and maintaining large-scale domain ontologies and knowledge graphs.
• Understanding of ontology governance, versioning, and data quality standards.
• Experience with graph-based data integration, reasoning, and semantic technologies.
• Strong problem-solving skills and ability to design scalable graph data architectures.