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Summer 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 ...

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 ...

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 ...

ITBMS Inc.

AI Engineer

Alpharetta, GA · On-site

Technical Skills AI & Agents LLMs, Agentic AI, Multi-Agent Systems, Prompt Engineering, Retrieval-Augmented Generation (RAG), Semantic Kernel, Lang-Graph, Microsoft Auto-Gen, AI Governance ...

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Summer Retrieval Augmented Generation information

What is a summer retrieval augmented generation role?

A Summer Retrieval Augmented Generation (RAG) role typically refers to a summer position focused on developing or improving retrieval-augmented generation systems, which are AI models that combine information retrieval with generative capabilities. In this role, you might work on integrating search algorithms with large language models, enabling systems to fetch relevant information from external sources and generate accurate, context-aware responses. These positions are often found in research labs, tech companies, or startups working on advanced AI applications, and are ideal for students or early-career professionals interested in machine learning, natural language processing, and AI research.

What are some common challenges faced when working on retrieval-augmented generation (RAG) projects during a summer internship?

During a summer internship focused on Retrieval-Augmented Generation (RAG), interns often encounter challenges such as integrating retrieval systems with generative models, managing large-scale datasets, and optimizing latency for real-time responses. Collaboration with cross-functional teams—including data engineers, research scientists, and product managers—is essential for aligning project goals and troubleshooting implementation issues. Additionally, interns may need to balance exploratory research with delivering usable prototypes within tight timeframes, which helps develop both technical and project management skills.

What are the key skills and qualifications needed to thrive as a retrieval augmented generation (RAG) engineer, and why are they important?

To thrive as a Retrieval Augmented Generation (RAG) Engineer, you need a strong background in machine learning, natural language processing, and information retrieval, typically supported by a degree in computer science or a related field. Proficiency with frameworks like PyTorch or TensorFlow, experience with vector databases (e.g., FAISS, Pinecone), and familiarity with LLM APIs are commonly required. Creative problem-solving, strong communication, and the ability to collaborate across multidisciplinary teams are essential soft skills. These competencies ensure effective development, deployment, and optimization of advanced AI systems that integrate retrieval and generative capabilities.

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:

Summer Retrieval Augmented Generation jobs near you

Agentic AI Engineer

Oraapps Inc

Atlanta, GA • On-site

Other

Posted 6 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)

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