Graph AI Engineer

Position: Graph AI Engineer
Location: Richardson TX/Hybrid model
Duration: longterm contract

ONLY LOCAL CANDIDATES TO TEXAS

Key Responsibilities

• Ontology & Knowledge Graph Engineering
• Design and Modeling: Lead the design and implementation of formal ontologies and semantic models to accurately represent complex business entities and their relationships.
• KG Implementation: Execute the build-out and continuous enrichment of the Customer Knowledge Graph, integrating data from from Different Data systems, transaction databases, marketing platforms, and other customer touchpoints.
• Relationship Discovery: Implement algorithms and analytical processes to detect hidden relationships such as corporate family structures, shared personnel, common addresses, buying groups, and influence networks.
• Graph Query Development: Write optimized queries against the graph database to support relationship analysis, pattern detection, and feature extraction for downstream applications.
• Platform Operations: Monitor, maintain, and tune the performance and scalability of the graph database to ensure high availability and efficient data access.

Experience

• 8+ years of hands-on technical experience in Data Engineering, Software Development, or Analytics.
• 2+ years dedicated hands-on experience in Knowledge Graph development and relationship mapping, preferably with customer or client data.

Technical Skills

• Knowledge Graph/Ontology: Deep practical expertise in graph data modeling, ontology development, and semantic modeling principles (RDF, RDFS, OWL, SHACL).
• Graph Databases: Proven hands-on experience with at least one major Graph Database technology such as Neo4j, AWS Neptune, TigerGraph, or JanusGraph, and expertise in native query languages (Cypher, SPARQL, or Gremlin).
• Graph Analytics: Strong experience with graph algorithms including community detection, centrality measures, path finding, pattern matching, and relationship scoring.
• Programming: Strong proficiency in Python for data manipulation, graph algorithm implementation, and data transformations.
• Data Engineering: Solid experience building scalable data pipelines using modern tools such as Apache Spark, Kafka, Airflow, or dbt.
• Customer Data: Understanding of customer master data management, and entity resolution techniques.
• Cloud & DevOps: Experience with version control (Git) and familiarity with CI/CD processes and major cloud platforms (AWS, Azure).