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

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)