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Internship Retrieval Augmented Generation Jobs in Georgia

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 ...

New

Marici Solutions

Palantir GEN AI Engineer

Atlanta, GA · On-site

Build retrieval-augmented generation (RAG) and ontology-augmented generation (OAG) workflows grounded in Foundry data. Develop copilots, decision-support agents and autonomous workflows using AIP ...

New

eTeam

Artificial Intelligence Engineer

Alpharetta, GA · On-site

$108K - $130K/yr

... Retrieval-Augmented Generation (RAG) and reasoning pipelines to ensure grounded, reliable, and adaptive agent behavior. • Collaborate closely with GenAI engineers, application teams, MLOps, product ...

Insight Global

AI Engineer

Atlanta, GA · On-site +1

... retrieval-augmented generation (RAG) and reranking; agent orchestration with LangGraph or comparable; or LLM fine-tuning. • Proficient in Python and comfortable working with async code, data ...

JR Software Solutions, Inc.

AI/ML Engineer

Atlanta, GA · On-site +1

Build and enhance Generative AI, LLM, and Retrieval-Augmented Generation (RAG) applications, including chatbot and conversational AI capabilities. * Develop and optimize data pipelines, feature ...

CYNET SYSTEMS

AI Engineer

Atlanta, GA · On-site

$50 - $55/hr

Develop and implement Retrieval Augmented Generation (RAG) solutions for knowledge retrieval and contextual AI responses. Design AI orchestration workflows using frameworks such as LangChain ...

CYNET SYSTEMS

AI Engineer

Atlanta, GA · On-site

$50 - $55/hr

Develop and implement Retrieval Augmented Generation (RAG) solutions for knowledge retrieval and contextual AI responses. * Design AI orchestration workflows using frameworks such as LangChain ...

Spruce Infotech

Agentic Gen AI Engineer

Alpharetta, GA · On-site

... Retrieval-Augmented Generation (RAG) and reasoning pipelines to ensure grounded, reliable, and adaptive agent behavior. • Collaborate closely with GenAI engineers, application teams, MLOps, product ...

Fisec Global

Gen AI Engineer

Atlanta, GA · On-site

... Retrieval Augmented Generation (RAG), Fine tuning LLMs, Prompt tuning, Graph RAGs, Knowledge graphs ... Experience mentoring junior colleagues and interns.

Florence Healthcare - US

Director of AI

Atlanta, GA

Guide engineers on best practices for Generative AI, Agentic AI, Retrieval-Augmented Generation (RAG), prompt engineering, and model integration. * Mentor AI engineers through technical coaching ...

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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.

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

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

What are popular job titles related to Internship Retrieval Augmented Generation jobs in Georgia?

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

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

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

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

Cities in Georgia 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 Georgia as of August 2026, with employment types broken down into 68% Full Time, 28% Part Time, 1% Temporary, and 3% Contract. Highlights an 67% Physical, 2% Hybrid, and 31% Remote job distribution.

Agentic AI Engineer

Oraapps Inc

Atlanta, GA • On-site

Other

Posted yesterday

New

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)

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