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Retrieval Augmented Generation Rag Jobs in Ashburn, VA

Closure Technologies

AI/ML Engineer

Mclean, VA · On-site

Closure Technologies is seeking a AI/ML Engineer who will Implement and maintain Retrieval-Augmented Generation (RAG) pipelines and integrate Large Language Models (LLMs) into applications, supported ...

Closure Technologies

AI/ML Engineer

Mclean, VA · On-site

Job Summary : Closure Technologies is seeking an AI/ML Engineer who will implement and maintain Retrieval-Augmented Generation (RAG) pipelines and integrate Large Language Models (LLMs) into ...

Closure Technologies

AI/ML Engineer

Mclean, VA · On-site

Job Summary : Closure Technologies is seeking an AI/ML Engineer who will implement and maintain Retrieval-Augmented Generation (RAG) pipelines and integrate Large Language Models (LLMs) into ...

ECS

Sr. Data Scientist

Fairfax, VA · On-site

$175K - $205K/yr

In this role, you will design and optimize next-generation search and retrieval experiences using Elasticsearch, Vector Search, and Retrieval-Augmented Generation (RAG) techniques to improve ...

Accenture Federal Services

AI Engineer

Arlington, VA · On-site

Architect and deploy Retrieval-Augmented Generation (RAG) solutions and AI orchestration frameworks * Manage Kubernetes-based AI deployments and ensure seamless integration with OpenAI-compatible ...

Accenture Federal Services

AI Engineer

Arlington, VA · On-site

Architect and deploy Retrieval-Augmented Generation (RAG) solutions and AI orchestration frameworks * Manage Kubernetes-based AI deployments and ensure seamless integration with OpenAI-compatible ...

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As of Jul 26, 2026, the average hourly pay for retrieval augmented generation rag in Ashburn, VA is $20.71, according to ZipRecruiter salary data. Most workers in this role earn between $17.69 and $21.63 per hour, depending on experience, location, and employer.
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AI/ML Engineer

Closure Technologies

Mclean, VA • On-site

Full-time

Posted 7 days ago

Job description

Closure Technologies is seeking a AI/ML Engineer who will Implement and maintain Retrieval-Augmented Generation (RAG) pipelines and integrate Large Language Models (LLMs) into applications, supported by API development and optimizing data storage through Postgres schema refinement.

Clearance Requirement: TS/SCI with Polygraph

Key Responsibilities:

  • Implement and maintain RAG pipelines, including document processing, embedding generation, retrieval configuration, and prompt assembly.
  • Integrate LLMs into applications using available APIs and frameworks.
  • Develop and maintain REST API interactions to support data retrieval and system integration.
  • Design or refine Postgres schemas to improve data organization and query performance.

Required Qualifications:

  • Demonstrated ability to conduct independent technical research, evaluate emerging AI/ML approaches, and apply advanced analytical problem-solving comparable to PhD-level research environments.
  • Ability to rapidly learn and apply new AI/ML methodologies, tools, and frameworks in support of evolving mission requirements.
  • Experience developing AI/ML applications focused on Retrieval-Augmented Generation (RAG), semantic retrieval, LLM integration, or related AI workflows.
  • Strong proficiency in Python and modern AI/ML libraries, frameworks, and API integrations.
  • Active/current TS/SCI with required polygraph.
  • Willingness to work onsite full time.
  • US citizenship required.
  • Senior Labor Category: Minimum 8 years of experience with a Bachelor's degree; or 7 years of experience with a Masters degree; or 6 years of experience with a Doctorate

Preferred Qualifications:

  • Advanced research experience in machine learning, deep learning, natural language processing, generative AI, reinforcement learning, computer vision, or related disciplines.
  • Experience publishing research, contributing to open-source AI/ML initiatives, or leading experimental and prototype development efforts.
  • Familiarity with model evaluation frameworks, fine-tuning workflows, inference optimization, and AI observability/monitoring tools.
  • Experience with vector databases, AWS/cloud environments, Docker, and containerized AI/ML development workflows.
  • Experience designing and integrating REST APIs and scalable data architectures.