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Knowledge Graph Semantic Jobs (NOW HIRING)

Squirro

Knowledge Graph Engineer

New York, NY · On-site

Semantic Modeling & Knowledge Graph Design: Develop and maintain taxonomies, ontologies, and classification models. Design semantic structures that align user needs with business objectives. * Cross ...

Squirro

Knowledge Graph Engineer

New York, NY · On-site

Semantic Modeling & Knowledge Graph Design: Develop and maintain taxonomies, ontologies, and classification models. Design semantic structures that align user needs with business objectives. * Cross ...

Squirro

Knowledge Graph Engineer

New York, NY · Remote

Semantic Modeling & Knowledge Graph Design: Develop and maintain taxonomies, ontologies, and classification models. Design semantic structures that align user needs with business objectives. * Cross ...

2T Consulting

NJ · On-site

We are seeking an experienced Ontology / Knowledge Graph Engineer with strong expertise in ontology engineering, semantic modeling, and knowledge graph development. The ideal candidate will have ...

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Knowledge Graph Semantic information

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$55.5K

$118.7K

$173.5K

How much do knowledge graph semantic jobs pay per year?

As of Sep 11, 2026, the average yearly pay for knowledge graph semantic in the United States is $118,674.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,500.00 and $133,500.00 per year, depending on experience, location, and employer.

What are knowledge graph semantics?

Knowledge Graph Semantics refer to the meaning and relationships among the concepts and entities represented within a knowledge graph. They define how data is connected, interpreted, and used to infer new knowledge, often by using ontologies and standardized vocabularies. Semantic technologies enable machines to understand the context and intent behind data, making it possible to perform advanced reasoning and query answering. This is crucial for applications like search engines, recommendation systems, and AI assistants that rely on structured and meaningful information.

What are the key skills and qualifications needed to thrive as a knowledge graph semantic engineer?

To thrive as a Knowledge Graph Semantic Engineer, you need expertise in ontology modeling, semantic web standards (like RDF, OWL, and SPARQL), and a background in computer science or information science. Familiarity with tools such as Protégé, graph databases (e.g., Neo4j, Stardog), and experience with data integration platforms or semantic reasoning engines is typically required. Strong analytical thinking, problem-solving, and the ability to communicate complex technical concepts to non-technical stakeholders are valuable soft skills in this role. These capabilities are essential for building, optimizing, and maintaining knowledge graphs that enable intelligent data connections and drive advanced analytics in organizations.

What are some common challenges faced when working as a knowledge graph semantic engineer, and how can they be addressed?

One of the main challenges for Knowledge Graph Semantic Engineers is integrating data from diverse sources while maintaining consistency and semantic accuracy. This often involves resolving conflicting information, mapping different data schemas, and ensuring that the graph reflects real-world relationships accurately. Collaboration with domain experts and data owners is crucial, as is staying updated on ontology standards and best practices. Regular validation, automated testing, and clear documentation can help address these challenges and ensure the integrity of the knowledge graph.

What is the difference between Knowledge Graph Semantic vs Data Scientist?

AspectKnowledge Graph SemanticData Scientist
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of ontologies and semantic technologiesDegree in Statistics, Computer Science, or related; proficiency in programming and statistical analysis
Work EnvironmentResearch and development teams, data integration projects, semantic web applicationsData analysis, modeling, and visualization in various industries like finance, tech, healthcare
Employer & Industry UsageTech companies, AI firms, knowledge management organizationsTech companies, consulting firms, research institutions

Knowledge Graph Semantic specialists focus on structuring data using ontologies and semantic technologies to enable intelligent data retrieval. Data Scientists analyze and interpret data to inform business decisions. While both roles work with data, Knowledge Graph Semantic roles emphasize data organization and semantics, whereas Data Scientists focus on analysis and modeling.

What other helpful pages are available for Knowledge Graph Semantic?

Other pages related to Knowledge Graph Semantic:

Infographic showing various Knowledge Graph Semantic job openings in the United States as of September 2026, with employment types broken down into 100% Full Time. Highlights an 75% In-person, and 25% Hybrid job distribution, with an average salary of $118,674 per year, or $57.1 per hour.

Principal GenAI Engineer - Knowledge Graph & Semantic Systems

New York, NY • On-site

SWITS DIGITAL Private Limited
See more jobs at SWITS DIGITAL Private Limitedexternal

Full-time

Re-posted 10 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

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