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Internship Retrieval Augmented Generation Jobs (NOW HIRING)

QTech US Inc

Gen AI Engineer

Miami Lakes, FL · On-site

Build and optimize Retrieval-Augmented Generation (RAG) solutions. Develop AI Agents and conversational AI systems. Integrate OpenAI, Azure OpenAI, Claude, Gemini, or Llama models into enterprise ...

Advantest

AI Intern

San Jose, CA

You will explore how retrieval-augmented generation (RAG) and agentic workflows can be used to analyze, compare, and assess complex technical content at scale. The internship emphasizes building AI ...

Datum Technologies LLC

AI / ML Engineer

Charlotte, NC · On-site

Implement LLM-based workflows, including prompt engineering, evaluation, and retrieval-augmented generation (RAG). * Build and maintain knowledge retrieval pipelines to support IVR use cases such as ...

Judge Group, Inc.

AI Architect

The Colony, TX · On-site

$170K - $180K/yr

Implement RAG (Retrieval Augmented Generation) patterns using requirements, user stories, APIs, configurations, and test repositories, leverage embeddings and vector search where applicable. Apply ...

Futran Tech Solutions Pvt. Ltd.

Data Scientist

Additionally, experience in building Retrieval-Augmented Generation (RAG) pipelines for search and chat applications is highly desired. Key Responsibilities: * Develop and optimize NLP models for ...

Closure Technologies

AI/ML Engineer

Mclean, VA

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 ...

ClifyX

AI Architect

Plano, TX · On-site

GPT, Claude • Prompt Engineering • RAG (Retrieval Augmented Generation) • AWS Cloud • Strong architectural and hands on GenAI expertise • Experience with enterprise automation and testing ...

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Internship Retrieval Augmented Generation information

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 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 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 June 2026, with employment types broken down into 25% Internship, 50% Full Time, and 25% Part Time. Highlights an 100% In-person job distribution.

Python / Generative AI Engineer

Prophecy Technologies

Los Angeles, CA • Hybrid

Full-time

Posted 25 days ago

Job description

JOB SUMMARY
The Python / Generative AI Engineer will design, develop, and implement AI-driven applications leveraging Generative AI technologies. The role focuses on building scalable AI solutions using Python, developing Retrieval-Augmented Generation (RAG) systems, and integrating AI models within cloud environments. The engineer will work closely with data scientists, engineers, and DevOps teams to implement GenAI solutions, optimize prompts, and deploy AI applications using modern cloud and container technologies.
Location
Los Angeles, CA / Irvine, CA (Hybrid)
Experience
5+ Years
Key Responsibilities
• Design and develop scalable applications using Python and SQL.
• Implement Generative AI solutions leveraging modern AI frameworks and tools.
• Build and maintain Retrieval-Augmented Generation (RAG) systems for AI-powered applications.
• Develop prompt engineering strategies to improve GenAI model outputs.
• Integrate AI solutions with cloud platforms and enterprise systems.
• Deploy and manage AI workloads using AWS, Docker, and DevOps pipelines.
• Collaborate with cross-functional teams to translate business problems into AI-driven solutions.
• Optimize AI workflows and ensure performance, reliability, and scalability.
• Implement and manage the Generative AI lifecycle including development, testing, deployment, and monitoring.
• Troubleshoot and resolve issues related to AI model integration and deployment.
Required Skills & Experience
• Minimum 5+ years of strong hands-on experience in Python development.
• Strong proficiency in SQL for data processing and analysis.
• Hands-on experience in Generative AI development using Python.
• Experience building Retrieval-Augmented Generation (RAG) based AI systems.
• Strong knowledge of prompt engineering and GenAI model interaction.
• Experience with AWS cloud services.
• Experience with containerization technologies such as Docker.
• Familiarity with DevOps practices and CI/CD pipelines.
• Strong analytical thinking, problem-solving, and critical reasoning skills.
• Ability to work independently with strong ownership and accountability.
Competencies
• Python Development
• Generative AI Development
• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• Cloud Computing (AWS)
• DevOps & Containerization
• SQL & Data Processing
Preferred Skills
• Experience using LangChain for building AI agents and GenAI workflows.
• Experience designing enterprise-level AI applications.
• Exposure to AI/ML model lifecycle management and deployment frameworks.

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