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Llm Ml Rag Jobs in Georgia (NOW HIRING)

Five and Fly

Machine Learning Engineer

Atlanta, GA · On-site

$120 - $165/hr

Contribute to the design, training, fine‑tuning, and deployment of ML/LLM models for production. * Implement RAG pipelines using vector databases. * Work with frameworks like LangChain, LangGraph ...

Sumeru

Senior AI/ML Engineer

Atlanta, GA · On-site

$100K - $138K/yr

You will also contribute to LLM-based solutions that enable natural language querying of CDP data ... Build and support RAG pipelines to enrich customer profiles with contextual data from unstructured ...

Five and Fly

Machine Learning Engineer

Atlanta, GA · On-site

Contribute to the design, training, fine-tuning, and deployment of ML/LLM models for production. * Implement RAG pipelines using vector databases. * Work with frameworks like LangChain, LangGraph ...

Elevance Health

Gen AI Engineer

Atlanta, GA · Hybrid

... ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG), NLP/LLM ...

Elevance Health

Gen AI Engineer

Atlanta, GA · On-site

... ML, Gen AI, NLP, LLM Models for batch and stream processing-based AI ML pipelines including data ingestion, preprocessing modules, search and retrieval, Retrieval Augmented Generation (RAG), NLP/LLM ...

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Llm Ml Rag information

What are some typical challenges faced when working on retrieval-augmented generation (RAG) systems in large language model (LLM) machine learning roles?

Professionals working on LLM ML RAG systems often encounter challenges such as ensuring the accuracy and relevancy of retrieved documents, managing latency for real-time queries, and seamlessly integrating retrieval mechanisms with generation models. Additionally, keeping up with evolving datasets and maintaining high-quality knowledge bases can be demanding. Collaboration with data engineers and domain experts is common to refine retrieval pipelines and optimize the end-to-end system.

What is the difference between Llm Ml Rag vs Data Scientist?

AspectLlm Ml RagData Scientist
Required CredentialsMaster's or PhD in ML, AI, or related fields; certifications in ML frameworksDegree in Computer Science, Statistics, or related; certifications in data analysis or ML
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, research, product development teams
Employer & Industry UsageTech firms, AI startups, research institutionsFinance, healthcare, tech, consulting firms
Common Search & ComparisonOften compared for ML specialization and research focusCompared for data analysis, modeling, and business insights

While both roles involve working with machine learning, Llm Ml Rag typically focuses on research and development of large language models, requiring advanced ML expertise. Data Scientists often work on analyzing data, building predictive models, and deriving insights for business decisions. The roles overlap in skills but differ in focus and application areas.

What are the key skills and qualifications needed to thrive as an llm ml rag engineer, and why are they important?

To excel as an LLM ML RAG Engineer, you need a strong background in machine learning, natural language processing, and large language models, typically supported by a degree in computer science or a related field. Proficiency with tools and frameworks like Python, PyTorch/TensorFlow, Hugging Face Transformers, and vector databases (e.g., FAISS, Pinecone) is essential, along with experience in deploying and fine-tuning LLMs and integrating retrieval systems. Strong problem-solving skills, attention to detail, and the ability to collaborate with cross-functional teams distinguish top performers in this role. These skills ensure the effective development and deployment of advanced AI solutions that combine generative and retrieval capabilities for high-impact applications.

What is an llm ml rag job?

LLM ML RAG jobs involve working with Large Language Models (LLMs), Machine Learning (ML), and Retrieval-Augmented Generation (RAG) systems. Professionals in these roles typically design, develop, and optimize AI systems that combine language models with retrieval techniques to improve accuracy, relevance, and factual grounding in generated outputs. These jobs often require expertise in natural language processing, deep learning, data engineering, and information retrieval. Key responsibilities might include integrating RAG pipelines, fine-tuning LLMs, and ensuring high-quality responses from AI applications.

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Llm Ml Rag jobs near you

GenAI Engineer (LLM Applications & RAG Architecture) - Q3-2026

R2 Technologies Corporation

Alpharetta, GA • On-site

Full-time

Posted 4 days ago

Job description

Overview:
About R2 Technologies: R2 Technologies is a Certified Minority Business Enterprise (MBE) headquartered in Alpharetta, GA. With over two decades of experience across global markets, we provide IT staffing and digital product engineering services to clients ranging from startups to Fortune 1000 companies. In addition to talent services, R2 develops proprietary solutions including SmartEnt, an enterprise AI and IoT intelligence platform. We work closely with our clients to deliver technology outcomes that are realistic, measurable, and impactful.
Job Summary: Most enterprises have proven that LLMs work in a demo-far fewer have gotten them into production. R2 Technologies is seeking a GenAI Engineer to close that gap. You will design and build LLM-powered applications on enterprise data: document ingestion and structured extraction pipelines, retrieval-augmented generation architectures, prompt and orchestration layers, and the backend services that expose them to business users. This role supports client engagements across financial services, healthcare, and retail, as well as our internal SmartEnt platform.
Key Responsibilities:
  • Design and develop production LLM applications, including chatbots, copilots, document intelligence, and summarization workflows.
  • Build end-to-end RAG pipelines covering document ingestion, chunking strategy, embedding generation, hybrid retrieval, and response grounding.
  • Develop and optimize prompt engineering workflows and LLM service orchestration layers across multiple model providers.
  • Integrate foundation models through Amazon Bedrock, Azure OpenAI, and direct LLM APIs, with routing logic for cost and latency optimization.
  • Build scalable Python backend services and REST APIs that expose GenAI capabilities to enterprise applications.
  • Evaluate model output quality through structured testing, hallucination detection, and response benchmarking against gold datasets.

Qualifications:
  • 3 years of experience in software engineering, AI/ML, or applied LLM development.
  • Advanced programming skills in Python, with additional experience in Node.js, Java, or TypeScript.
  • Hands-on expertise with Amazon Bedrock, Azure OpenAI, or direct integration with LLM APIs and model services.
  • Proven experience designing RAG architectures using vector databases such as Pinecone, Weaviate, ChromaDB, FAISS, or Azure AI Search.
  • Strong knowledge of embeddings, prompt engineering, chunking strategies, and model evaluation techniques.
  • Experience building REST APIs and microservices, with working knowledge of SQL and NoSQL databases and cloud deployment on AWS, Azure, or GCP.

Skills:
NODE.JS,PYTHON,AZURE,JAVA,AI,DATABASES,NOSQL,SQL,TYPESCRIPT,AWS

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