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Anblicks Linkedin · Posted 8d ago

Lead AI Engineer

Dallas, Texas, United States

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Job Description – Lead AI Engineer (Knowledge Graph / Ontology & Agentic AI)

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)
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