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Internship Retrieval Augmented Generation Jobs in Virginia

Altagrove LLC

Data Science

Norfolk, VA · On-site

Design and implement Large Language Model (LLM) solutions, Retrieval-Augmented Generation (RAG) architectures, vector databases, and AI-enabled knowledge management capabilities. * Develop scalable ...

Argo Cyber Systems

AI/ML Automation Engineer

Arlington, VA · On-site

$120K - $135K/yr

Build Retrieval-Augmented Generation (RAG) pipelines for secure knowledge retrieval. * Design prompt engineering strategies and optimize AI model performance. * Develop feature engineering, data ...

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Internship Retrieval Augmented Generation information

What is an internship in Retrieval Augmented Generation (RAG)?

An Internship in Retrieval Augmented Generation (RAG) is a temporary position, typically for students or early-career professionals, focused on developing or researching AI systems that combine information retrieval with generative models. Interns in this field may work on enhancing how AI models find and use external data sources to generate accurate, context-aware responses. This role often involves tasks such as data preprocessing, implementing retrieval algorithms, fine-tuning language models, and evaluating system performance. It offers valuable hands-on experience with cutting-edge AI technologies and frameworks.

What types of projects or tasks can I expect to work on during an internship in Retrieval Augmented Generation (RAG)?

As an intern in Retrieval Augmented Generation, you can expect to work on projects that involve integrating information retrieval systems with generative AI models. Typical tasks may include curating and preprocessing data sets, developing or fine-tuning retrieval algorithms, evaluating the performance of RAG pipelines, and collaborating with engineers and researchers to improve end-to-end system accuracy. You may also assist in conducting experiments, analyzing results, and documenting findings, all within a collaborative team environment that values innovation and knowledge sharing.

What are the key skills and qualifications needed to thrive as an intern working with Retrieval Augmented Generation (RAG), and why are they important?

To thrive as an intern in Retrieval Augmented Generation, you need a foundational understanding of natural language processing, machine learning concepts, and strong programming skills, often supported by coursework or research in computer science or data science. Familiarity with tools like Python, PyTorch or TensorFlow, and experience with libraries such as Hugging Face Transformers and vector databases are typically required. Strong analytical thinking, curiosity, and effective communication make candidates stand out in collaborative, research-intensive environments. These abilities are critical for developing, evaluating, and improving RAG systems that combine information retrieval with generative models.

What is the difference between Internship Retrieval Augmented Generation vs Internship Data Analyst?

AspectInternship Retrieval Augmented GenerationInternship Data Analyst
Required SkillsKnowledge of AI, NLP, retrieval systems, programmingData analysis, statistical skills, Excel, SQL
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, marketing departments
Employer UsageDevelop AI models, improve retrieval systemsAnalyze data trends, generate reports

Internship Retrieval Augmented Generation focuses on developing AI models that combine retrieval systems with language generation, requiring skills in AI and programming. In contrast, an Internship Data Analyst concentrates on analyzing data sets to inform business decisions, emphasizing statistical and analytical skills. Both roles are common in tech and business sectors but serve different functions within organizations.

What are the most commonly searched types of Retrieval Augmented Generation jobs in Virginia?

The most popular types of Retrieval Augmented Generation jobs in Virginia are:

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For Internship Retrieval Augmented Generation jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Internship Retrieval Augmented Generation jobs in Virginia look for?

The top searched job categories for Internship Retrieval Augmented Generation jobs in Virginia are:

What cities in Virginia are hiring for Internship Retrieval Augmented Generation jobs?

Cities in Virginia with the most Internship Retrieval Augmented Generation job openings:

Internship Retrieval Augmented Generation jobs near you
Infographic showing various Internship Retrieval Augmented Generation job openings in Virginia as of June 2026, with employment types broken down into 7% Internship, 68% Full Time, 7% Part Time, and 18% Contract. Highlights an 71% Physical, 2% Hybrid, and 27% Remote job distribution.

ID Software/Application Engineer Professional (408)

Environmental and Safety Solutions, Inc.

Herndon, VA • On-site

$59 - $77/hr

Contractor

Posted 24 days ago

Job description

Job Summary:
We are seeking a highly skilled Senior Agentic AI Engineer to design, develop, and deploy modern Agentic AI solutions. This role focuses on building production-grade Generative AI applications, multi-agent workflows, and Retrieval-Augmented Generation (RAG) pipelines for enterprise use on Microsoft Azure.

You will collaborate with architects, software engineers, data engineers, and business stakeholders to translate requirements into AI-powered software solutions. The ideal candidate brings hands-on experience developing, evaluating, and operating Agentic AI solutions, supported by strong front-end and back-end engineering fundamentals.
 
Major Responsibilities:
  • Build Generative AI & Retrieval-Augmented Generation LLM Applications.
  • Build LLM-powered applications for text generation, summarization, Q&A, conversational AI, enterprise knowledge search, and multi-agent orchestration.
  • Develop advanced RAG pipelines using embeddings, Azure AI Search vector and hybrid retrieval, document chunking, metadata filtering, reranking, citations, and grounding techniques with enterprise data.
  • Build secure, reliable integrations between AI agents and enterprise tools, REST APIs, relational databases, and event-driven services.
  • Develop and maintain user-facing AI application experiences using React and TypeScript, and supporting application services using Node.js or comparable back-end technologies.

AI Agents & Agentic Automation:
  • Design and implement single-agent and multi-agent systems for intelligent automation, decisioning, and complex workflows.
  • Build autonomous and human-in-the-loop agents that plan, reason, act, and interact with tools, APIs, enterprise data, and event-driven systems.
  • Develop agentic workflows using Microsoft Agent Framework, Azure AI Foundry services, or comparable modern orchestration frameworks.
  • Implement configuration-driven agent behavior, prompt and tool management, authorization boundaries, and resilient error-handling patterns.
  • Define and automate evaluation approaches for agent quality, including groundedness, relevance, citation quality, safety, and regression testing.
  • Instrument agent workflows for traces, tool calls, latency, token usage, errors, and operational metrics using OpenTelemetry, Application Insights, or comparable observability platforms.
  • Build highly scalable, secure, containerized solutions with CI/CD, health checks, horizontal scaling, and production monitoring.
     
Education and Experience Requirements:
  • Requires a bachelor's degree (or international equivalent) and 8+ years of relevant software engineering experience.
  • 2-3 years of hands-on Generative AI, LLM application, or Agentic AI solution development experience.
  • Strong software engineering background with experience designing and deploying production-grade cloud applications.
  • Experience building front-end applications with React and TypeScript, and back-end services with Node.js or comparable application frameworks.
  • Hands-on experience building Generative AI and RAG applications with Azure AI Foundry, Azure OpenAI, Azure AI Search, LLM APIs, embeddings, vector or hybrid search, knowledge retrieval, grounding, and citations.
  • Experience with Agentic AI frameworks such as Microsoft Agent Framework, Semantic Kernel, LangGraph, AutoGen, or comparable orchestration frameworks; including single-agent and multi-agent systems, tool-calling workflows, and human-in-the-loop controls.
  • Experience evaluating and improving agent quality, including prompt engineering, test datasets, LLM-based evaluation, safety checks, and production feedback loops.
  • Strong knowledge of LLMOps, CI/CD, containerization (Docker and Kubernetes), observability, and production operations for AI applications.
  • Good understanding of RESTful API principles, asynchronous application patterns, secure integrations, relational databases, SQL, and data-access patterns; familiarity with SQL/NoSQL data stores and data engineering or ETL pipelines.

Experience working in an enterprise environment with large-scale, secure AI deployments, including identity, authorization, data privacy, compliance, and production monitoring.

Strong analytical, problem-solving, collaboration, and communication skills.
Must be a US Citizenship or Green card holder.