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Ai Rag Jobs in Virginia (NOW HIRING)

Node.Digital

Senior AI/ML Engineer

Herndon, VA · On-site

$107.50K - $147.60K/yr

Experience with generative AI models and frameworks (LLMs, RAG architectures, prompt engineering, model fne-tuning) * Hands-on exp with OCR, ICR and OMR technologies is a must * Good programming ...

Node.Digital

Senior AI/ML Engineer

Herndon, VA · On-site

$107.50K - $147.60K/yr

Experience with generative AI models and frameworks (LLMs, RAG architectures, prompt engineering, model fne-tuning) * Hands-on exp with OCR, ICR and OMR technologies is a must * Good programming ...

Leidos

Agentic AI Developer

Chantilly, VA

$69.55K - $125.73K/yr

Develop AI-enabled applications using large language models and retrieval-augmented generation (RAG) architectures. Integrate AI agents with APIs, databases, enterprise systems, and external services.

Leidos

Agentic AI Developer

Chantilly, VA · On-site

$69.55K - $125.73K/yr

... RAG) architectures. • Integrate AI agents with APIs, databases, enterprise systems, and external services. • Build conversational and human-in-the-loop interfaces for AI systems. • Develop ...

Node.Digital

Senior AI/ML Engineer

Herndon, VA · On-site +1

$107.50K - $147.60K/yr

Experience with generative AI models and frameworks (LLMs, RAG architectures, prompt engineering, model fne-tuning) * Hands-on exp with OCR, ICR and OMR technologies is a must * Good programming ...

Guidehouse

AI Engineer and Architect

Mclean, VA

The AI Engineer is accountable for automated metadata harvesting and documentation, delivering ... RAG / knowledge integration for semantic discovery: Lead work that connects harvested metadata to ...

Guidehouse

AI Engineer and Architect

Arlington, VA

The AI Engineer is accountable for automated metadata harvesting and documentation, delivering ... RAG / knowledge integration for semantic discovery: Lead work that connects harvested metadata to ...

Guidehouse

AI Engineer and Architect

Mclean, VA · On-site

The AI Engineer is accountable for automated metadata harvesting and documentation, delivering ... RAG / knowledge integration for semantic discovery: Lead work that connects harvested metadata to ...

Tech Tammina LLC

AI/ML Engineer

Reston, VA · On-site

AI/ML Engineer Location: Reston VA Core Responsibilities (AI/ML, Python, AWS, GenAI) Design and ... Architect and operationalize RAG pipelines, embeddings, vector databases, and LLM‑powered ...

DLA Piper

AI Solutions Engineer

Reston, VA · On-site

Summary The AI Solutions Engineer is an embedded partner to the Professional Excellence (PE ... Understanding of ML models, ability to select the correct models and solution patterns, various RAG ...

Prophecy Technologies

Gen AI Solution Architect

Mclean, VA · On-site

$63.75 - $84/hr

Design AI solutions using vector databases for semantic search and RAG (Retrieval-Augmented Generation) . * Implement AI workloads on cloud platforms and scalable infrastructure . * Optimize AI ...

Dlapiper

AI Solutions Engineer

Reston, VA · Hybrid

Summary The AI Solutions Engineer is an embedded partner to the Professional Excellence (PE ... Understanding of ML models, ability to select the correct models and solution patterns, various RAG ...

Steampunk

AI Developer

Mclean, VA

$145K - $185K/yr

This role will provide expert guidance on the architecture, design, development, and delivery of enterprise-grade AI applications--spanning LLM-powered systems, RAG pipelines, agents, multimodal ...

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Ai Rag information

What are the key skills and qualifications needed to thrive as an AI Researcher, and why are they important?

To thrive as an AI Researcher, you need a strong background in computer science, mathematics, and machine learning, usually with an advanced degree such as a Master's or Ph.D. Proficiency with programming languages like Python, deep learning frameworks (e.g., TensorFlow, PyTorch), and familiarity with scientific research tools is essential. Critical thinking, creativity, and effective collaboration are vital soft skills for generating novel ideas and working in multidisciplinary teams. These skills and qualities are crucial to drive innovation and solve complex problems in the rapidly evolving field of artificial intelligence.

What are some common challenges faced by AI RAG (Retrieval-Augmented Generation) engineers when integrating retrieval systems with large language models?

AI RAG engineers often encounter challenges such as ensuring seamless integration between retrieval systems and language models, maintaining low latency for real-time responses, and handling the quality and relevance of retrieved data. Additionally, tuning the system to balance retrieval accuracy with generative fluency can be complex, especially when dealing with large or unstructured datasets. Collaboration with data engineers, ML researchers, and product teams is essential to address these challenges and optimize system performance.

What are AI RAGs?

AI RAGs, or Retrieval-Augmented Generation systems, are a type of artificial intelligence that combines the power of retrieving information from large databases or documents with generating human-like text responses. This approach allows AI models to provide more accurate, up-to-date, and contextually relevant answers by referencing external data sources during the generation process. RAGs are commonly used in applications like chatbots, search engines, and customer support systems, where comprehensive and factual responses are important.

What is the difference between Ai Rag vs Data Analyst?

AspectAi RagData Analyst
Required CredentialsTypically a diploma or certification in AI, machine learning, or related fieldsBachelor's degree in statistics, mathematics, or related fields
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, healthcare, and various industries
Employer & Industry UsagePrimarily in AI development and researchAcross industries for data interpretation and decision-making
Common Search & ComparisonYesYes

Ai Rag and Data Analyst roles share overlapping skills in data handling and analysis, but Ai Rag focuses more on AI-specific applications and machine learning, while Data Analysts concentrate on interpreting data to inform business decisions. Both roles are vital in data-driven industries, with Ai Rag often working in AI development environments and Data Analysts supporting strategic insights across sectors.

What are popular job titles related to Ai Rag jobs in Virginia? For Ai Rag jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Ai Rag jobs in Virginia look for? The top searched job categories for Ai Rag jobs in Virginia are:
What cities in Virginia are hiring for Ai Rag jobs? Cities in Virginia with the most Ai Rag job openings:
Ai Rag jobs near you
AI Engineer

AI Engineer

International Logic Systems, Inc. (ILS)

Fairfax, VA • Hybrid

Other

Posted 21 days ago

Job description

AI Engineer - required onsite (hybrid) in Fairfax, VA

Overview

ILS Inc. is seeking an AI Engineer to design and implement next-generation AI solutions using large language models (LLMs), agentic workflows, and Model Context Protocol (MCP)-based integrations. The ideal candidate has hands-on experience building AI-powered applications (not just training models) and understands how to orchestrate tools, APIs, and data sources into reliable systems. 

MUST BE LOCAL TO DC METRO AREA (hybrid support - 2 days in ILS HQ office, located in Fairfax, VA 22033).

Responsibilities

  • Design and build agentic AI systems that can reason, plan, and execute tasks using LLMs.
  • Implement integrations using Model Context Protocol (MCP) to connect AI agents with tools, APIs, and enterprise systems.
  • Develop and maintain LLM-powered applications (e.g., copilots, chat systems, automation tools).
  •  Build prompt pipelines, tool-calling workflows, and multi-step reasoning systems.
  • Develop backend services using Java (Spring Boot) and/or Python to expose AI capabilities via APIs.
  • Leverage Spring AI or similar frameworks to integrate LLMs into enterprise applications.
  • Implement RAG (Retrieval-Augmented Generation) pipelines using vector databases.
  • Integrate AI solutions with cloud-native services such as AWS, Azure, and Google Cloud.
  •  Deploy AI services and ensure scalability, reliability, and performance.
  • Collaborate with cross-functional teams in an Agile/Scrum environment.
  • Monitor and optimize AI systems for latency, cost, and output quality.

Qualifications

Required skill sets - Extensive experience with:

  • Bachelor’s degree in computer science, Engineering, or related field.
  • 10  years of professional experience in software engineering, 2-3 years in AI engineering, or applied ML roles.
  • Hands-on experience with LLM APIs.
  •  Experience building agent-based workflows.
  • ·Understanding of prompt engineering, tool usage, and structured outputs.
  •  Familiarity with RAG architectures and vector databases.
  • Experience with Model Context Protocol (MCP) or similar integration standards.
  • Experience working with at least one cloud platform and exposure to others:
    • AWS (Lambda, Bedrock, SageMaker, S3)
    • Azure (Azure OpenAI, Functions, Cognitive Services)
    • Google Cloud (Gemini Enterprise Agent Platform, Vertex AI, Cloud Functions, BigQuery)

Preferred Skills

  • Strong programming skills in Python and/or Java.
  • Experience building backend services using Spring Boot (REST APIs, microservices).
  • Exposure to Spring AI or similar frameworks for integrating LLMs into Java applications.
  • Experience with Docker and containerized deployments.
  • Basic frontend experience (React, Angular) for AI-driven applications.
  • Experience working in regulated environments (government, finance, healthcare).

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