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Knowledge Graph Jobs in Virginia (NOW HIRING)

Senior AI Engineer

Dahlgren, VA · On-site

$106K - $146K/yr

TSC is seeking a Senior Ontology Engineer to design, develop, and maintain ontological frameworks and knowledge graph solutions in support of defense mission engineering programs. The ideal candidate ...

Responsibilities : • Lead end-to-end knowledge graph and knowledge base development efforts from problem definition to production, designing pipelines that extract, normalize, link, and organize ...

Work with internal teams to integrate AI components with semantic layers and knowledge graph implementations * Security and Access Control: Design, configure, and advise clientele on production-grade ...

NLP Engineer with Security Clearance

Herndon, VA · On-site

$117K - $141K/yr

As an NLP Engineer at BTI360, you will: • Lead end-to-end knowledge graph and knowledgebase development efforts from problem definition to production, designing pipelines that extract, normalize ...

Work with internal teams to integrate AI components with semantic layers and knowledge graph implementations * Security and Access Control: Design, configure, and advise clientele on production-grade ...

Deep experience with graph representation learning, graph transformers (e.g., GCN/GAT/GraphSAGE), spatio-temporal GNNs, heterogeneous graphs (HGNN/Relational GNNs), and knowledge-graph-augmented ...

Deep experience with graph representation learning, graph transformers (e.g., GCN/GAT/GraphSAGE), spatio-temporal GNNs, heterogeneous graphs (HGNN/Relational GNNs), and knowledge-graph-augmented ...

Deep experience with graph representation learning, graph transformers (e.g., GCN/GAT/GraphSAGE), spatio-temporal GNNs, heterogeneous graphs (HGNN/Relational GNNs), and knowledge-graph-augmented ...

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Knowledge Graph information

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.

Is ML a high paying job?

Machine Learning (ML) roles, including positions like ML engineer or data scientist, are generally well-paid due to the specialized skills required, such as programming, statistics, and knowledge of algorithms. Salaries tend to be higher than average in tech hubs and often increase with experience, certifications, and proficiency in tools like Python, TensorFlow, or PyTorch.

What is a knowledge graph job description?

A knowledge graph job description typically involves designing, developing, and maintaining knowledge graphs that organize and connect data for improved search, reasoning, and data integration. The role often requires skills in data modeling, graph databases like Neo4j, and understanding of semantic technologies such as RDF and OWL. Professionals in this field may work with data scientists, software engineers, and domain experts to ensure accurate and efficient knowledge representation.

What is a Knowledge Graph job?

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 is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as AI research director, senior machine learning engineer, or AI product executive, often requiring advanced skills in data science, programming, and deep learning. These roles usually involve leadership, strategic planning, and expertise in tools like TensorFlow or PyTorch, with compensation reflecting experience and impact. Such salaries are rare and generally found in top tech companies or specialized AI firms.

What engineer makes $500,000 a year?

Senior data engineers or machine learning engineers working in high-demand industries such as technology, finance, or AI can earn salaries around $500,000 annually, especially with extensive experience, advanced skills in big data tools, and relevant certifications. Compensation varies based on location, company size, and individual expertise.

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.

What are the most commonly searched types of Knowledge Graph jobs in Virginia? The most popular types of Knowledge Graph jobs in Virginia are:
What are popular job titles related to Knowledge Graph jobs in Virginia? For Knowledge Graph jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Knowledge Graph jobs in Virginia look for? The top searched job categories for Knowledge Graph jobs in Virginia are:
Infographic showing various Knowledge Graph job openings in Virginia as of July 2026, with employment types broken down into 1% Locum Tenens, 64% Full Time, 30% Part Time, 3% Contract, and 2% Summer. Highlights an 63% Physical, 3% Hybrid, and 34% Remote job distribution.
Knowledge Graph Software Engineer with Security Clearance

Knowledge Graph Software Engineer with Security Clearance

BTI360 Inc

Herndon, VA • On-site

Other

Posted 25 days ago


Job description

Knowledge Graph Software Engineer About the Team
Here at BTI360, we’ve built a culture that’s passionate about developing software engineers. Software doesn't build itself. People do.  In fact, teams of people do.  That's why our primary focus is on developing better craftsmen, better teammates, and better technical leaders.  By putting people first, we're not just giving our teammates more opportunities to grow, we're also raising the bar of the software we ship. BTI360 previously has been voted 10 years in a row as a TOP Place to Work by the Washington Business Journal.  BTI360 is seeking an Knowledge Graph Software Engineer who is passionate about transforming raw data into meaningful insights that drive strategic decision‑making. In this role, you’ll work closely with our engineers and mission teams to tackle complex business problems using emerging AI technologies —specifically by extracting and linking critical entities to build and curate scalable graph databases and knowledge bases. This role sits at the intersection of backend software engineering, knowledge representation, and applied AI.
 
What you will do in this role: As a Knowledege Graph Software Engineer at BTI360, you will: • Lead end-to-end knowledge graph and knowledge base development efforts from problem definition to production, designing pipelines that extract, normalize, link, and organize information into scalable graph-based systems.  • Design and evaluate extraction and resolution workflows using sound methodologies and fit-for-purpose metrics to assess entity extraction, linking, relationship quality, and overall knowledgebase completeness and accuracy.  • Translate business requirements into quantitative problems and communicate technical findings to both technical and non-technical stakeholders through reports, presentations, and direct customer engagement.  • Drive technical decision-making for schema design, ontology alignment, extraction approaches, and graph architecture based on mission needs, data quality, and long-term maintainability.  • Stay current with advances in knowledge representation and information extraction and introduce practical techniques, tools, and frameworks that improve graph construction, curation, and analytic value.  • Apply analytical and statistical methods to validate extracted insights, measure data quality, and support confident decision-making from structured and unstructured sources.  • Develop reports and whitepapers that evaluate solution alternatives based on impact, cost, technical feasibility, and alignment with strategic goals.  • Collaborate across teams to align on strategy, provide data science expertise, and contribute to proposals and strategic initiatives.   • Mentor junior data scientists by providing technical guidance, defining project direction, and sharing best practices in graph-oriented data modeling, extraction workflows, and knowledgebase stewardship.  The ideal candidate should possess the following skills: • Active Security Clearance (Secret or higher) or the ability to obtain one • Hands-on experience with graph databases such as Amazon Neptune, Neo4j, or related graph technologies. • Experience with source control (e.g. Git) and CI/CD pipeline tools such as AWS Code Build (preferred), Jenkins, GitLab CI, or GitHub Actions • Experience designing and implementing scalable, maintainable, and OOP based software in a containerized cloud environment (AWS preferred) leveraging foundational services for computing, identity management, and networking. • Experience developing backend services using Java and the Spring/Spring Boot framework (or similar relevant Java framework) • Experience with testing frameworks such as Junit (preferred), Mockito, or Spring Runner • Familiarity with API standards such as REST and HTTP, message-driven architectures, persistent storage layers, and distributed systems • Effective written and verbal communication skills necessary to perform job duties and collaborate with team members • Candidates must maintain a primary residence within a two-hour drive of Herndon, VA to support onsite collaboration as needed. Desired skills: • Familiarity with monitoring and observability stacks such as Prometheus/Grafana (preferred), CloudWatch, or ELK/EFK • Contributions to open-source libraries or community projects or personal projects • Experience with search technologies such as OpenSearch (preferred), Elasticsearch, or Solr • Experience working with streaming or event-driven architectures such as SNS/SQS (preferred), Kafka, Kinesis, AWS Step Functions, or Event Bridge • Knowledge of Infrastructure as Code (e.g., Terraform) and how to leverage DevSecOps pipelines to deliver code • Exposure to additional programming languages including shell scripting languages (e.g. Python, NodeJS, C#, Bash, PowerShell) • Exposure to modern AI-assisted software development workflows, including agentic development frameworks and LLM tooling • Familiarity with monitoring and observability stacks including Open Telemetry, Splunk (preferred), Prometheus/Grafana, or CloudWatch The base salary pay range for this role is $125,900 - $247,900. This range reflects base compensation only and does not include benefits, bonuses, or additional pay incentives associated with the opportunity.