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Semantic Web Jobs (NOW HIRING)

Maxar

$115K - $151K/yr

Leveraging Semantic Web standards, including RDF, OWL, and SPARQL, the LeadOntologistdevelops scalable knowledge frameworks that enhance data discoverability, support AI/ML applications, and ...

CELESTAR

Lead Ontologist - OBIQUA

Reston, VA · On-site

... semantic web standards (e.g., RDF, OWL, SPARQL) and government data standards to enable semantic representation of data inputs, facilitating integration and interoperability across intelligence ...

TetraScience

Data & Semantic Model Architect

$65.25 - $84/hr

Deep, hands-on expertise with semantic web standards (RDF, OWL, SHACL, SPARQL) and property graph concepts (LPG). Requirements * 7+ years of experience in data architecture, informatics, or technical ...

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How much do semantic web jobs pay per hour?

As of Jun 23, 2026, the average hourly pay for semantic web in the United States is $59.01, according to ZipRecruiter salary data. Most workers in this role earn between $51.20 and $66.83 per hour, depending on experience, location, and employer.

What is Semantic Web?

The Semantic Web is an extension of the current web that enables data to be shared and reused across applications through standardized formats like RDF and OWL. Professionals working in this field develop ontologies, linked data, and semantic technologies to improve data integration and machine understanding. Skills in web development, data modeling, and knowledge of semantic standards are essential for roles in this area.

What are the typical daily responsibilities of a Semantic Web professional?

A Semantic Web professional typically spends their day designing semantic data models, developing ontologies, and implementing standards like RDF and OWL to structure and link complex information. You may work closely with data scientists, software engineers, and subject matter experts to ensure data interoperability and support knowledge-driven applications. Tasks often include writing SPARQL queries, integrating heterogeneous data sources, and collaborating on system architecture or solution design. This role frequently involves staying updated with the latest Semantic Web standards and tools, ensuring that data solutions are robust, scalable, and aligned with organizational needs.

How much do Semantic Web engineers make?

Semantic Web engineers typically earn between $80,000 and $130,000 annually, depending on experience, location, and specific skills such as ontology development and knowledge graph management. Senior roles or those with expertise in related technologies like RDF, OWL, and SPARQL can command higher salaries.

What is a Semantic Web job?

A Semantic Web job involves working with technologies that enhance data interoperability and meaning on the web. Professionals in this field use standards like RDF, OWL, and SPARQL to structure and link data, making it machine-readable and semantically rich. Roles may include developing knowledge graphs, ontologies, and linked data applications to improve search, AI, and data integration. These jobs are common in industries like AI, research, and enterprise data management.

Is RDF still used?

Yes, RDF (Resource Description Framework) remains a fundamental technology in the Semantic Web for representing and exchanging structured data. It is widely used in data integration, knowledge graphs, and linked data applications, often alongside tools like SPARQL for querying. RDF's standards are maintained by the W3C and continue to be relevant in semantic data modeling roles.

Which IT field is most in demand?

The IT field most in demand currently includes roles related to data science, cybersecurity, cloud computing, and software development. Skills in machine learning, cloud platforms like AWS or Azure, and programming languages such as Python are highly sought after by employers.

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

To thrive in a Semantic Web role, you need a strong background in computer science, expertise in data modeling, ontologies, and RDF/OWL, often supported by a relevant degree. Familiarity with semantic technologies such as SPARQL, Protégé, and graph databases, as well as certifications in linked data or Semantic Web technologies, is highly beneficial. Strong analytical thinking, problem-solving skills, and the ability to work effectively in interdisciplinary teams are standout soft skills. These abilities are crucial for designing, implementing, and maintaining systems that enable smarter data integration and retrieval across complex datasets.

More about Semantic Web jobs
What cities are hiring for Semantic Web jobs? Cities with the most Semantic Web job openings:
What are the most commonly searched types of Semantic Web jobs? The most popular types of Semantic Web jobs are:
What states have the most Semantic Web jobs? States with the most job openings for Semantic Web jobs include:
What job categories do people searching Semantic Web jobs look for? The top searched job categories for Semantic Web jobs are:
Semantic Web jobs near you
Infographic showing various Semantic Web job openings in the United States as of June 2026, with employment types broken down into 1% Internship, 1% As Needed, 23% Full Time, 55% Part Time, 19% Contract, and 1% Nights. Highlights an 83% Physical, 4% Hybrid, and 13% Remote job distribution, with an average salary of $122,736 per year, or $59 per hour.

Agentic AI / Semantic Solutions Architect

TekDallas

Atlanta, GA

Other

Posted 4 days ago

Job description

Job Title: Agentic AI / Semantic Solutions Architect
Location: Atlanta, GA (Hybrid)
Duration: Long Term Contract

Job Summary

We are seeking an experienced Agentic AI / Semantic Solutions Architect to design and develop AI-powered solutions leveraging Large Language Models (LLMs), Knowledge Graphs, Semantic Technologies, and GraphRAG architectures. The ideal candidate will have strong experience building AI agents, semantic search solutions, metadata-driven systems, and enterprise AI applications using modern AI frameworks and cloud technologies.

Responsibilities
  • Design and implement Agentic AI and multi-agent architectures for enterprise applications.
  • Develop GraphRAG solutions combining vector search and knowledge graph retrieval.
  • Build AI workflows using LangChain, LangGraph, LlamaIndex, AutoGen, or similar frameworks.
  • Design semantic data models, ontologies, metadata frameworks, and knowledge graphs.
  • Develop prompt engineering and context management strategies for LLM applications.
  • Integrate AI solutions with enterprise data platforms and APIs.
  • Build and optimize vector search and semantic retrieval systems.
  • Collaborate with business and technical teams to deliver AI-driven solutions.
  • Create proof-of-concepts and scalable AI architectures.
  • Ensure AI solutions follow security, governance, and best practices.
Required Skills
  • 10+ years of overall IT experience.
  • Strong experience with Agentic AI, Generative AI, and Large Language Models (LLMs).
  • Hands-on experience with LangChain, LangGraph, LlamaIndex, AutoGen, or similar AI frameworks.
  • Experience with Retrieval-Augmented Generation (RAG) and GraphRAG architectures.
  • Strong understanding of Knowledge Graphs, Ontologies, Semantic Modeling, and Metadata Management.
  • Experience with Prompt Engineering and Context Engineering.
  • Knowledge of Model Context Protocol (MCP).
  • Experience with Vector Databases such as Pinecone, Weaviate, ChromaDB, pgvector, or Vertex AI Vector Search.
  • Programming experience in Python.
  • Experience with REST APIs, microservices, and cloud platforms.
  • Strong understanding of NLP, Machine Learning, and AI solution architecture.
  • Experience with Git, CI/CD, and Agile methodologies.
Preferred Skills
  • Experience with Google Cloud Platform (Google Cloud Platform) and Vertex AI.
  • Knowledge of Spanner Graph, Dataplex, or Collibra.
  • Experience with Neo4j, RDF, SPARQL, Graph Databases, and Semantic Web technologies.
  • Experience in Financial Services, Banking, Insurance, or regulated industries.
  • Knowledge of AI governance, security, and compliance frameworks.

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