Knowledge Graph AI Engineer

We are seeking a highly analytical and hands-on Senior Engineer specializing in Ontology and Knowledge Graph (KG) implementation. This pivotal role focuses on designing and operationalizing a Customer & Client Knowledge Graph to map complex business relationships, identify hidden connections across our customer base, and surface actionable insights that drive revenue growth and customer engagement. The ideal candidate will be an expert in graph data modeling, relationship analytics, and scalable graph database solutions.

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.

Required Qualifications and Skills

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).