Location: Lebanon, NJ
Salary: $145,000.00 USD Annually - $155,000.00 USD Annually
Description: Knowledge Graph & AI EngineerOverviewWe are seeking an experienced Knowledge Graph & AI Engineer with deep expertise in graph technologies, ontology design, and modern AI integration. This role focuses on building scalable knowledge graph architectures, developing graph queries, integrating structured and semi-structured data, and enabling AI-driven insights through RAG and LLM-powered applications. The ideal candidate has experience in data engineering, AI engineering, graph data modeling, and deploying machine learning solutions in cloud environments.
Responsibilities- Design and implement knowledge graph architectures using property graph or RDF-based models.
- Transform and integrate structured and semi-structured data into optimized graph structures.
- Develop and query graph systems using Cypher and/or SPARQL.
- Design ontologies and entity-relationship models to support sales intelligence and related use cases.
- Integrate knowledge graphs with LLMs using Retrieval-Augmented Generation (RAG) patterns.
- Build APIs and backend services to deliver AI-driven prospecting and recommendation insights.
- Implement scoring models, relationship strength analytics, and graph traversal logic.
- Ensure scalability, security, and performance across enterprise systems.
- Partner with sales, business, and engineering teams to translate requirements into technical solutions.
Required Qualifications- 5+ years of experience in data engineering, AI engineering, or knowledge graph development.
- Hands-on experience with graph technologies, including property graph and/or RDF frameworks.
- Proficiency with Cypher and/or SPARQL.
- Strong data modeling and ontology design skills.
- Experience integrating data from relational databases and external sources.
- Strong Python development experience.
- Experience integrating LLMs into enterprise applications.
- Familiarity with Retrieval-Augmented Generation (RAG) architectures and AI-driven recommendation systems.
- Experience with Amazon SageMaker, AWS Machine Learning tools, ML pipelines, MLOps, CI/CD, model deployment, and inference endpoints.
- Experience with Docker, Kubernetes, and EKS.
- Industry experience in insurance, claims, underwriting, or fraud detection.
Preferred Qualifications- Experience building sales intelligence or CRM-adjacent platforms.
- Knowledge of embeddings, semantic search, and vector databases.
- Experience designing relationship scoring or network analytics models.
- Cloud experience with AWS, Azure, or Google Cloud Platform.
- Experience working in financial services or insurance environments.
Success Criteria- Deliver a scalable knowledge graph and AI solution that improves sales prospect identification and relationship insights.
- Demonstrate measurable increases in targeting precision and cross-sell opportunity discovery.
- Establish reusable architectural patterns to support enterprise AI-driven sales initiatives.
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