ZipRecruiter

Log In

1

Knowledge Graph Jobs in Virginia (NOW HIRING)

Hatch IT

Sr. Director, Generative & Agentic AI

Reston, VA · Hybrid

$210K - $240K/yr

You will focus on capabilities that automate analysis, reduce cognitive load, and power Babel Street's future Knowledge Graph. A defining aspect of this role is ensuring all AI capabilities are ...

Enterprise Knowledge

Semantic Modeler

Arlington, VA · On-site

$120K - $135K/yr

Willingness and ability to develop new skills, especially ontology and knowledge graph development Preferred Skills and Experience: * Knowledge of best practices and approaches for search analytics

Guidehouse

Metadata / Data Cataloging Senior Analyst

Mclean, VA · On-site

$86K - $109K/yr

... knowledge-graph relationships, and catalog curation practices). This role also leads hands-on evaluation and selection of catalog solutions (AoA) and supports pilots that prove capabilities such as ...

Guidehouse

Metadata / Data Cataloging Senior Analyst

Mclean, VA · On-site

$86K - $109K/yr

... knowledge-graph relationships, and catalog curation practices). This role also leads hands-on evaluation and selection of catalog solutions (AoA) and supports pilots that prove capabilities such as ...

Virginia Tech

Postdoctoral Associate

Blacksburg, VA · On-site

Responsibilities : • Contribute to building AI-native frameworks that combine physics-based modeling, machine-learning methods, knowledge-graph and ontology-based scientific data infrastructures ...

previous page

Showing results 41-60

All JobsKnowledge Graph JobsKnowledge Graph Jobs in Virginia

Knowledge Graph information

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.

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 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:

Knowledge Graph jobs near you
Infographic showing various Knowledge Graph job openings in Virginia as of August 2026, with employment types broken down into 1% As Needed, 77% Full Time, 18% Part Time, 1% Temporary, and 3% Contract. Highlights an 91% Physical, 2% Hybrid, and 7% Remote job distribution.

Sr. Director, Generative & Agentic AI

Hatch IT

Reston, VA • Hybrid

$210K - $240K/yr

Full-time

Re-posted 24 days ago

Job description

hatch I.T. is partnering with Babel Street to find a Sr. Director, Generative & Agentic AI. Please see details below:

About the Role

As Senior Director of Generative & Agentic AI, you will play a central role in Babel Street’s transformation into an AI-native risk intelligence organization. You will architect, operationalize, and scale generative and agentic AI capabilities across the Babel Street platform, ensuring they are mission-ready, safe, economically efficient, and deeply integrated with their products and data ecosystem. 

You will work closely with the President & Chief AI Officer, as well as Product and Engineering leadership to execute the company’s AI strategy—balancing innovation with rigor, speed with safety, and capability with cost discipline. This role requires strong technical depth, hands-on experience, architectural judgment, and the ability to translate rapidly evolving AI technologies into reliable, customer-facing intelligence capabilities. You will lead teams building multilingual and multi-modal LLM and SLM pipelines, retrieval, agent-driven workflow systems, and graph-integrated reasoning capabilities that directly support intelligence applications—powering investigative, analytical, and operational workflows across Babel Street’s product suite. 

You will focus on capabilities that automate analysis, reduce cognitive load, and power Babel Street’s future Knowledge Graph. A defining aspect of this role is ensuring all AI capabilities are delivered with strong guardrails, low hallucination rates, transparent behavior, and a relentless focus on winning on AI economics. 

This is a hybrid role to be based out of either their Reston, VA or Somerville MA office.

About the Company

Babel Street is the trusted technology partner for the world’s most advanced identity intelligence and risk operations. They deliver advanced AI and data analytics solutions providing unmatched, analysis-ready data regardless of language, proactive risk identification, 360-degree insights, high-speed automation, and seamless integration into existing systems. Babel Street empowers government and commercial organizations to transform high-stakes identity and risk operations into a strategic advantage.  The actionable insights we deliver safeguard lives and protect critical assets around the world.  Babel Street is headquartered in Reston, Virginia, with regional offices in Boston, MA and Cleveland, OH, and international offices in Australia, Canada, Israel, Japan, and the U.K.

Role Span:

This role spans three integrated domains: 

Generative AI & LLM/SLM Platform  

You will help define and execute Babel Street’s generative AI strategy, establishing clear frameworks for when to deploy multilingual and multi-modal LLMs versus SLMs and when to apply RAG versus fine-tuning to maximize accuracy, explainability, and mission impact. You will shape tooling and vendor decisions, lead model-provider partnerships, and architect scalable multilingual inference pipelines optimized through quantization, caching, routing, and GPU efficiency to ensure we consistently win on AI economics. You will integrate embeddings and retrieval systems, develop evaluation and red-teaming pipelines, and ensure all generative capabilities meet mission-grade requirements for reliability, transparency, cost efficiency, and global language coverage. 

Agentic AI & Workflow Automation  

You will design and implement agent architectures that deliver mission-aligned automation directly to customers—accelerating investigations, reducing cognitive load, and enabling intelligence applications and cross-product task execution through LLM- and agent-powered Knowledge Graph intelligence. In parallel, you will collaborate with Engineering teams to introduce agentic capabilities that improve engineering velocity, automate internal workflows, enhance data quality, and streamline operations. You will operationalize agentic SDLC practices, build evaluation and guardrail systems, and establish observability frameworks to ensure reliable, secure, and transparent agent behavior, with a strong emphasis on cost-efficient execution and orchestration. 

AI Integration & Safe, Secure Delivery 

You will collaborate with Product and Engineering to embed AI-native practices into the product suite and support the integration of AI capabilities into user-facing workflows. In this role, you will help productionize AI features through governance, telemetry, automated evaluation, adversarial testing, and responsible AI frameworks. You will contribute to the design and implementation of safeguards, guardrails, and hallucination-mitigation techniques, and support controls that monitor drift, enforce safe model behavior, and maintain transparency across the AI lifecycle—ensuring AI capabilities are measurable, trustworthy, and aligned with emerging regulatory expectations. 

Across all domains, you will help build a high-performing AI organization and foster a culture defined by velocity, craftsmanship, safety, experimentation, and outcome-driven execution. 

What you will do:

Generative AI & LLM/SLM Platform

  • Execute the generative AI strategy, including LLM vs. SLM and RAG vs. fine-tuning decision frameworks.
  • Architect scalable, multilingual inference pipelines optimized for performance, reliability, and AI economics.
  • Lead model tooling selection and partnerships across proprietary and open-weight ecosystems.

Agentic AI & Workflow Automation

  • Design agent architectures that automate investigations and cross-platform analytical workflows.
  • Build agentic systems that leverage the Knowledge Graph for reasoning, task planning, and orchestration.
  • Introduce agentic capabilities that improve internal engineering and operational workflows. 

AI Integration & Safe, Secure Delivery

  • Support Product and Engineering teams in embedding AI capabilities into customer workflows.
  • Contribute to AI productization through governance, telemetry, automated testing, and adversarial evaluation.
  • Help design and implement safeguards, guardrails, and drift-monitoring controls. 

Organizational Leadership & Collaboration

  • Lead and develop AI engineers and applied scientists through mentorship and technical leadership. 
  • Partner closely with Engineering, Product, and Data leaders to ensure aligned execution.
  • Represent AI capabilities and strategy internally and, as needed, with customers and partners. 
What you will bring:
  • 10+ years of experience in AI/ML, applied analytics, or advanced data systems, including 3–5 years leading technical teams delivering production GenAI capabilities.
  • Demonstrated experience applying generative and agentic AI capabilities to intelligence applications, including investigative analysis, entity and relationship discovery, pattern detection, and analyst-driven workflows in high-stakes or mission-critical environments.
  • Strong technical expertise in agentic AI, workflow automation, orchestration frameworks, and evaluation techniques.
  • Experience designing systems that minimize hallucinations and enforce safe, predictable AI behavior.
  • Hands-on experience with cloud-native AI infrastructure, inference optimization, and cost-aware system design.
  • Ability to translate complex AI concepts into practical, mission-ready product capabilities.
  • Strong communication skills and the ability to collaborate effectively across technical and non-technical teams.
  • Experience operating in regulated, high-stakes, or mission-critical environments is strongly preferred. 

Education

  • Bachelor’s degree in Computer Science, Engineering, AI/ML, or a related technical field required. 
    Master’s degree or PhD preferred. 

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.