Lead AI Engineer (Knowledge Graph / Ontology & Agentic AI)

Job Description – Lead AI Engineer (Knowledge Graph / Ontology & Agentic AI) Location: Dallas, TX Role Summary Seeking a Lead AI Engineer with strong expertise in Knowledge Graph (KG), Ontology modeling, and Generative AI (LLMs, Agentic AI) to design and scale a Customer Knowledge Graph platform using Neo4j and App Orchid. The role will lead AI product/platform development, enabling relationship intelligence, Customer 360 insights, and AI-driven decisioning. Key Responsibilities Knowledge Graph & Ontology (Neo4j / App Orchid) • Design and implement ontology models and semantic frameworks • Build and scale Customer Knowledge Graph using Neo4j and App Orchid • Develop entity resolution, relationship mapping, and enrichment pipelines • Write and optimize graph queries (Cypher) for analytics and insights • Manage performance, scalability, and governance of KG platform AI & Agentic AI Development • Architect and implement Agentic AI and multi-agent systems • Leverage LLMs and RAG with Knowledge Graph for contextual intelligence • Enable capabilities such as: • Customer 360 insights • Relationship discovery & scoring • Natural language querying (Graph/SQL agents) • Drive end-to-end AI lifecycle (design → deploy → optimize) Data Engineering & Integration • Build scalable pipelines to integrate enterprise data into KG • Implement customer identity resolution and data quality frameworks • Design APIs for application and AI model integration Leadership & Platform Ownership • Lead AI platform architecture and roadmap • Mentor engineering teams and enforce best practices • Drive AI-first SDLC adoption and enterprise scaling • Collaborate with business, data science, and engineering stakeholders Required Skills • Knowledge Graph & Ontology: RDF, OWL, semantic modeling • Graph Platforms: Strong hands-on with Neo4j and App Orchid • Graph Querying: Cypher (mandatory) • AI/GenAI: LLMs, RAG, Agentic AI (CrewAI/LangGraph) • Programming: Python (AI + data engineering) • Data Engineering: Spark, Kafka, Airflow (or equivalent) • Cloud: AWS / Azure • MLOps/DevOps: CI/CD, scalable system design Preferred Skills • Customer 360 / Customer Data Platforms • Graph analytics (community detection, centrality) • Graph visualization tools • Exposure to GNNs • Docker / Kubernetes Leadership Expectations • Own AI product/platform delivery end-to-end • Define technical roadmap and architecture strategy • Drive enterprise AI adoption with business impact (revenue, engagement)