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Internship Retrieval Augmented Generation Jobs (NOW HIRING)

Oraapps Inc

Agentic AI Engineer

Atlanta, GA · On-site

Develop LLM-powered applications leveraging Retrieval-Augmented Generation (RAG), tool calling, and orchestration frameworks. * Build scalable APIs, microservices, and integrations supporting ...

Pereview Software, LLC.

AI Engineer

Dallas, TX · On-site

$90 - $120/hr

Develop and maintain Retrieval-Augmented Generation (RAG) architectures using vector databases and semantic search technologies * Create, test, and refine prompts, structured outputs, and evaluation ...

PB consulting

AI Programmer Analyst

Corona, NY

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

PB consulting

AI Programmer Analyst

Staten Island, NY · On-site

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

PB consulting

AI Programmer Analyst

Elmwood Park, NJ · On-site

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

PB consulting

AI Programmer Analyst

Staten Island, NY · On-site

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

PB consulting

AI Programmer Analyst

Bloomfield, NJ · On-site

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

PB consulting

AI Programmer Analyst

Bronx, NY · On-site

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

PB consulting

AI Programmer Analyst

College Point, NY · On-site

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

Cliff Services Inc

Qdrant Developer

Alpharetta, GA · On-site

Knowledge of RAG (Retrieval-Augmented Generation) architectures. * Experience integrating Qdrant with LLM frameworks such as LangChain or LlamaIndex. * Familiarity with REST APIs and microservices.

Pronix Inc

Python AI Developer

Malvern, PA · On-site

$49.25 - $68/hr

Experience designing and implementing Retrieval-Augmented Generation (RAG) solutions * Hands-on experience with LangChain or similar AI orchestration frameworks * Experience with AWS services such as:

PB consulting

AI Programmer Analyst

Teterboro, NJ

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

PB consulting

AI Programmer Analyst

Belleville, NJ · On-site

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

PB consulting

NY · On-site

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

PB consulting

AI Programmer Analyst

Cliffside Park, NJ · On-site

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

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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.

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Internship Retrieval Augmented Generation jobs near you
Infographic showing various Internship Retrieval Augmented Generation job openings in the United States as of August 2026, with employment types broken down into 65% Full Time, 33% Part Time, and 2% Contract. Highlights an 63% Physical, 3% Hybrid, and 34% Remote job distribution.

Agentic AI Engineer

Oraapps Inc

Atlanta, GA • On-site

Other

Posted 10 days ago

Job description

What You''ll Do

  • Design, develop, and deploy enterprise-grade AI applications using modern software engineering best practices.
  • Build and enhance agentic AI solutions, AI copilots, and intelligent workflow automation.
  • Develop LLM-powered applications leveraging Retrieval-Augmented Generation (RAG), tool calling, and orchestration frameworks.
  • Build scalable APIs, microservices, and integrations supporting enterprise AI platforms.
  • Collaborate with data scientists to productionize machine learning and AI solutions.
  • Implement testing, monitoring, observability, and governance practices for AI applications.
  • Ensure AI solutions meet security, compliance, and responsible AI standards.
  • Contribute to architecture decisions for enterprise AI platforms and reusable application frameworks.
  • Work within Azure and Microsoft''s AI ecosystem while supporting multi-cloud best practices where appropriate.
  • Participate in code reviews and promote engineering excellence across the team.

Current AI Initiatives

  • This role will contribute to several strategic AI initiatives, including:
  • Payer Intelligence Platform
  • Monitor payer policy changes using AI.
  • Assess operational impact of policy updates.
  • Support managed care teams in prioritizing actions and dispute resolution.
    • Clinical Chart Review: Build agentic AI solutions using EHR and clinical documentation.
  • Support patient cohort identification.
  • Generate clinical insights for quality improvement initiatives.
  • Population Market Intelligence: Analyze internal and external datasets.
  • Generate recommendations for service line growth.
  • Identify emerging healthcare market opportunities.

Required Qualifications

  • MUST HAVE A Bachelor''s degree in Computer Science, Engineering, Data Science, or a related technical field. Master''s degree preferred. Equivalent professional experience may be considered in lieu of an advanced degree.
  • Approximately 3+ years of experience in AI engineering, machine learning engineering, data engineering, software engineering, or a related technical discipline.
  • At least 2 years of experience designing, building, and supporting production-grade enterprise applications.
  • Hands-on experience developing applications using Large Language Models (LLMs).
  • Experience implementing Retrieval-Augmented Generation (RAG) architectures.
  • Experience building agentic AI applications, AI assistants, or workflow automation solutions.
  • Strong Python programming skills.
  • Experience building and consuming RESTful APIs.
  • Knowledge of software engineering best practices, including testing, version control, CI/CD, and maintainable application design.
  • Experience designing scalable enterprise application architectures.

Preferred Qualifications

  • Experience within healthcare, provider organizations, payer organizations, or biomedical environments.
  • Experience with Microsoft Azure and Azure AI services.
  • Familiarity with GitHub Copilot and the Microsoft AI ecosystem.
  • Experience with AI governance, responsible AI practices, observability, guardrails, and model monitoring.
  • Background in MLOps and production AI deployment.
  • Technical Environment
  • Python
  • Azure (preferred)
  • GitHub Copilot
  • Microsoft AI ecosystem
  • REST APIs
  • Microservices
  • Enterprise AI architecture
  • Tool-calling frameworks
  • Retrieval-Augmented Generation (RAG)

Apply