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Retrieval Augmented Generation Jobs in Atlanta, GA

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

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

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 ...

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 ...

... 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 ...

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

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 ...

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

Develop retrieval-augmented generation pipelines, integrate enterprise data sources, and manage vector databases. * Deploy solutions, monitor performance, troubleshoot issues, and update them to ...

Develop retrieval-augmented generation pipelines, integrate enterprise data sources, and manage vector databases. * Deploy solutions, monitor performance, troubleshoot issues, and update them to ...

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Retrieval Augmented Generation information

What is a retrieval augmented generation?

A Retrieval Augmented Generation (RAG) job typically involves developing and optimizing AI systems that enhance text generation by incorporating external knowledge retrieved from relevant sources. Professionals in this field work on integrating retrieval mechanisms with large language models to improve the relevance, accuracy, and factual grounding of generated content. Common responsibilities include designing retrieval systems, fine-tuning language models, optimizing performance, and ensuring the seamless integration of factual data into AI-generated text. This role is highly interdisciplinary, involving expertise in natural language processing (NLP), machine learning, and information retrieval.

What does a retrieval augmented generation engineer do?

A Retrieval Augmented Generation engineer typically spends their day designing and implementing systems that combine information retrieval with advanced generative models, such as large language models. This includes fine-tuning models, integrating external data sources, developing vector search pipelines, and evaluating output quality. Collaboration with data scientists, machine learning engineers, and product teams is common to ensure the solutions meet user requirements and scale effectively. Additionally, RAG engineers often troubleshoot issues, monitor model performance in production, and stay informed about the latest advancements in AI and information retrieval.

What skills and qualifications are needed for retrieval augmented generation?

To thrive in a Retrieval Augmented Generation (RAG) engineering role, you need a solid background in machine learning, natural language processing (NLP), and experience with scalable information retrieval systems, typically supported by a relevant degree in computer science or a related field. Familiarity with tools such as Python, PyTorch or TensorFlow, vector databases, and search platforms like Elasticsearch is essential, along with practical experience deploying and tuning RAG pipelines. Strong problem-solving skills, a collaborative mindset, and effective communication abilities set outstanding professionals apart in this field. These competencies are crucial for designing, implementing, and optimizing hybrid retrieval-generation AI systems that address complex, real-world information needs.

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The most popular types of Retrieval Augmented Generation jobs in Atlanta, GA are:

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For Retrieval Augmented Generation jobs in Atlanta, GA, the most frequently searched job titles are:

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Cities near Atlanta, GA with the most Retrieval Augmented Generation job openings:

Infographic showing various Retrieval Augmented Generation job openings in Atlanta, GA as of August 2026, with employment types broken down into 100% Full Time. Highlights an 100% Remote job distribution.

Agentic AI Engineer

Atlanta, GA • On-site

Oraapps Inc
IT Services • 51 - 200 employees

Other

Posted 15 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)