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$9

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How much do knowledge graph jobs pay per hour?

As of Aug 16, 2026, the average hourly pay for knowledge graph in the United States is $31.03, according to ZipRecruiter salary data. Most workers in this role earn between $15.87 and $25.96 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the knowledge graph position, and why are they important?

To thrive as a Knowledge Graph Engineer, you need strong skills in semantic web technologies, ontology modeling, and data integration, typically supported by a background in computer science or data science. Familiarity with tools like RDF, SPARQL, OWL, and knowledge graph platforms (e.g., Neo4j, GraphDB) is common, and certifications in data engineering or semantic technologies are beneficial. Effective communication, problem-solving abilities, and cross-functional collaboration are valuable soft skills in this field. These competencies are crucial for designing, implementing, and maintaining knowledge graphs that enable advanced data discovery and insights for organizations.

What is a knowledge graph?

A Knowledge Graph job typically involves designing, building, and maintaining structured representations of data that map relationships between entities. Professionals in this role work with technologies like RDF, SPARQL, ontologies, and graph databases to enhance data integration, retrieval, and reasoning. These jobs are common in AI, search, and data science fields, helping organizations improve knowledge discovery and decision-making.

What are some typical daily responsibilities of a knowledge graph engineer?

As a Knowledge Graph Engineer, your typical day involves designing and developing ontologies, integrating diverse data sources, and implementing graph-based data models to enhance information accessibility. You may work closely with data scientists, software developers, and business analysts to gather requirements and translate them into scalable knowledge graph solutions. Regular tasks include writing SPARQL queries, performing data mapping, maintaining documentation, and troubleshooting graph data issues. Collaboration and ongoing learning are integral as this field rapidly evolves with new tools and best practices.

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Knowledge Graph jobs near you
Infographic showing various Knowledge Graph job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 17% Part Time, 1% Temporary, and 3% Contract. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution, with an average salary of $64,550 per year, or $31 per hour.

Principal GenAI Engineer - Knowledge Graph & Semantic Systems

SWITS DIGITAL Private Limited

New York, NY • On-site

Full-time

Re-posted 14 days ago

Job description

Role: Principal GenAI Engineer - Knowledge Graph & Semantic Systems
Location: Onsite - NYC
Job Type: Full-Time
About the Role
We are hiring a Principal GenAI Engineer with strong expertise in LLMs and Knowledge Graphs to lead enterprise-scale AI implementations for Fortune 500 clients. This role focuses on building Graph-powered RAG systems (Graph-RAG) that combine structured semantic reasoning with advanced LLM architectures to deliver scalable, explainable, production-grade AI solutions.
What We're Looking For
  • 14+ years of experience in ML/AI systems
  • 2+ years hands-on experience with LLMs (RAG, agents, prompt engineering)
  • 5+ years of production experience working with Knowledge Graphs
  • Strong proficiency in Python, LangChain/LangGraph, and SQL
  • Experience deploying GenAI systems on AWS / Azure / GCP

Mandatory Knowledge Graph Expertise
  • Design and scale enterprise Knowledge Graph architectures
  • Develop ontologies, taxonomies, and semantic data models
  • Implement entity resolution, relationship extraction, and graph enrichment
  • Experience with Neo4j, Amazon Neptune, or similar graph databases
  • Strong hands-on experience with Cypher (or similar graph query languages)
  • Build hybrid retrieval systems combining Knowledge Graphs + vector databases
  • Integrate structured graph reasoning with LLMs to reduce hallucination and improve explainability

Apply