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Ai Rag Jobs in Atlanta, GA (NOW HIRING)

Build RAG pipelines and AI agents. * Fine-tune and optimize machine learning models. * Deploy models using Docker, Kubernetes, and cloud platforms. * Create scalable REST APIs for AI services. * Work ...

... powered systems -- RAG pipelines, agent workflows, or fine-tuning -- in a production or ... AI -- PDF parsing, table extraction, or OCR pipelines. • Has contributed to or built an ...

Senior Agentic (AI) Engineer

Atlanta, GA · On-site +1

$100K - $138K/yr

Worth AI is hiring a Senior Agentic AI Engineer to design and ship production agent systems that ... Strong RAG fundamentals chunking, embeddings, hybrid retrieval, reranking, grounding - and judgment ...

Senior Agentic (AI) Engineer

Atlanta, GA · Remote

$107K - $146K/yr

Worth AI is hiring a Senior Agentic AI Engineer to design and ship production agent systems that ... Strong RAG fundamentals chunking, embeddings, hybrid retrieval, reranking, grounding -- and ...

AI Architect

Atlanta, GA · On-site

$60.50 - $79.75/hr

... g., RAG, agentic workflows, ML pipelines, rules + AI hybrids). • Creates reusable AI assets, templates, and architectural guardrails to accelerate delivery. • Establishes build vs. buy vs ...

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

... RAG) and knowledge-grounded AI solutions • Integrate AI agents with enterprise systems via REST APIs, databases, and cloud services • Build agent memory, tool usage, and prompt workflows for ...

Alpharetta, GA Job Type: Full-Time * Design and implement LLM-powered applications using RAG, embeddings, and agentic AI * Build scalable end-to-end AI architectures integrating enterprise data

Architect AI/ML solutions involving Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and LangChain (Lang). * Integrate and manage MCP Server for scalable AI infrastructure and ...

Alpharetta, GA Job Type: Full-Time * Design and implement LLM-powered applications using RAG, embeddings, and agentic AI * Build scalable end-to-end AI architectures integrating enterprise data

Showing results 21-40

Ai Rag information

See Atlanta, GA salary details

$30.8K

$56K

$80.3K

How much do ai rag jobs pay per year?

As of Aug 6, 2026, the average yearly pay for ai rag in Atlanta, GA is $56,012.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,100.00 and $62,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an AI researcher?

To thrive as an AI Researcher, you need a strong background in computer science, mathematics, and machine learning, usually with an advanced degree such as a Master's or Ph.D. Proficiency with programming languages like Python, deep learning frameworks (e.g., TensorFlow, PyTorch), and familiarity with scientific research tools is essential. Critical thinking, creativity, and effective collaboration are vital soft skills for generating novel ideas and working in multidisciplinary teams. These skills and qualities are crucial to drive innovation and solve complex problems in the rapidly evolving field of artificial intelligence.

What is the difference between Ai Rag vs Data Analyst?

AspectAi RagData Analyst
Required CredentialsTypically a diploma or certification in AI, machine learning, or related fieldsBachelor's degree in statistics, mathematics, or related fields
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, healthcare, and various industries
Employer & Industry UsagePrimarily in AI development and researchAcross industries for data interpretation and decision-making
Common Search & ComparisonYesYes

Ai Rag and Data Analyst roles share overlapping skills in data handling and analysis, but Ai Rag focuses more on AI-specific applications and machine learning, while Data Analysts concentrate on interpreting data to inform business decisions. Both roles are vital in data-driven industries, with Ai Rag often working in AI development environments and Data Analysts supporting strategic insights across sectors.

What is an AI RAG?

AI RAGs, or Retrieval-Augmented Generation systems, are a type of artificial intelligence that combines the power of retrieving information from large databases or documents with generating human-like text responses. This approach allows AI models to provide more accurate, up-to-date, and contextually relevant answers by referencing external data sources during the generation process. RAGs are commonly used in applications like chatbots, search engines, and customer support systems, where comprehensive and factual responses are important.

What are common challenges faced by AI RAG engineers when integrating retrieval systems with large language models?

AI RAG engineers often encounter challenges such as ensuring seamless integration between retrieval systems and language models, maintaining low latency for real-time responses, and handling the quality and relevance of retrieved data. Additionally, tuning the system to balance retrieval accuracy with generative fluency can be complex, especially when dealing with large or unstructured datasets. Collaboration with data engineers, ML researchers, and product teams is essential to address these challenges and optimize system performance.
What are popular job titles related to Ai Rag jobs in Atlanta, GA? For Ai Rag jobs in Atlanta, GA, the most frequently searched job titles are:
What job categories do people searching Ai Rag jobs in Atlanta, GA look for? The top searched job categories for Ai Rag jobs in Atlanta, GA are:
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Posted 8 days ago


Job description

Job Title: AI/ML Engineer

Location: Atlanta, GA

Duration: Long Term

Responsibilities

  • Design, build, and deploy AI/ML models for business applications.
  • Develop NLP and Generative AI solutions using LLMs.
  • Build RAG pipelines and AI agents.
  • Fine-tune and optimize machine learning models.
  • Deploy models using Docker, Kubernetes, and cloud platforms.
  • Create scalable REST APIs for AI services.
  • Work with data engineers to prepare training datasets.
  • Monitor model performance and retrain models when needed.
  • Collaborate with product managers and software engineers.
  • Follow MLOps best practices for model lifecycle management.

Required Skills

  • Python
  • Machine Learning algorithms (Supervised & Unsupervised Learning)
  • Deep Learning (TensorFlow, PyTorch, Keras)
  • Natural Language Processing (NLP)
  • Large Language Models (LLMs) OpenAI, Llama, Claude, Gemini
  • Prompt Engineering
  • Retrieval-Augmented Generation (RAG)
  • Vector Databases (Pinecone, FAISS, ChromaDB, Milvus)
  • LangChain or LlamaIndex
  • SQL and NoSQL databases
  • REST APIs and FastAPI/Flask
  • Git and CI/CD
  • AWS, Azure, or Google Cloud
  • Docker and Kubernetes
  • Data preprocessing and feature engineering