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Assistant Retrieval Augmented Generation Jobs in Georgia

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

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 Solution Architect

Atlanta, GA · On-site

$60.50 - $79.75/hr

Architect Generative AI, Agentic AI, RAG (Retrieval-Augmented Generation), LLM, and intelligent automation solutions. * Design scalable cloud-native AI architectures on AWS, Azure, or Google Cloud ...

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

Solid understanding of context handling, retrieval-augmented generation (RAG), and optimization techniques * Proficiency in Python and modern AI/ML frameworks (e.g., PyTorch, TensorFlow) * Experience ...

... solutions, retrieval-augmented generation (RAG), knowledge graph technologies, and emerging agentic AI frameworks. The position also supports AI governance, model lifecycle management, and the ...

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Assistant Retrieval Augmented Generation information

What is the difference between Assistant Retrieval Augmented Generation vs Data Analyst?

AspectAssistant Retrieval Augmented GenerationData Analyst
Required CredentialsKnowledge of AI, NLP, and retrieval systemsBachelor's in Statistics, Data Science, or related fields
Work EnvironmentTech companies, AI development teamsBusiness, finance, healthcare sectors
Industry UsageAI, machine learning, natural language processingData analysis, reporting, decision support

Assistant Retrieval Augmented Generation focuses on developing AI models that combine retrieval techniques with language generation, often requiring expertise in AI and NLP. Data Analysts interpret data to generate insights, primarily using statistical tools. While both roles involve working with data, Assistant Retrieval Augmented Generation is centered on AI model development, whereas Data Analysts focus on data interpretation and reporting.

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What cities in Georgia are hiring for Assistant Retrieval Augmented Generation jobs? Cities in Georgia with the most Assistant Retrieval Augmented Generation job openings:
Infographic showing various Assistant Retrieval Augmented Generation job openings in Georgia as of July 2026, with employment types broken down into 1% As Needed, 77% Full Time, 19% Part Time, 1% Temporary, and 2% Contract. Highlights an 98% Physical, 1% Hybrid, and 1% Remote job distribution.

Qdrant Developer

Cliff Services Inc

Alpharetta, GA • On-site

Other

Posted 9 days ago


Job description

Position: Qdrant Developer
Duration: 12+ Months

Interview Mode: Virtual

Job Description

We are seeking a skilled Qdrant Developer with hands-on experience in vector databases and AI-powered search applications. The ideal candidate should have experience designing, implementing, and optimizing vector search solutions using Qdrant for Retrieval-Augmented Generation (RAG) and semantic search use cases.

Required Skills

  • 3+ years of software development experience with Python.
  • Hands-on experience with Qdrant Vector Database.
  • Strong understanding of vector embeddings and semantic search.
  • Experience with embedding models such as OpenAI, Sentence Transformers, or Hugging Face.
  • Knowledge of RAG (Retrieval-Augmented Generation) architectures.
  • Experience integrating Qdrant with LLM frameworks such as LangChain or LlamaIndex.
  • Familiarity with REST APIs and microservices.
  • Experience with Docker and Kubernetes is a plus.
  • Knowledge of cloud platforms (AWS, Azure, or Google Cloud Platform).
  • Strong problem-solving and debugging skills.

Responsibilities

  • Design, develop, and maintain vector search solutions using Qdrant.
  • Build and optimize semantic search and RAG pipelines.
  • Create and manage vector collections, indexing, and embeddings.
  • Integrate Qdrant with AI/ML applications and LLM frameworks.
  • Optimize search performance, scalability, and data retrieval.
  • Collaborate with AI engineers, data scientists, and application developers.
  • Monitor, troubleshoot, and improve vector database performance.

Preferred Qualifications

  • Experience with Generative AI and Large Language Models (LLMs).
  • Knowledge of FastAPI or Flask.
  • Experience with Git, CI/CD, and Agile development methodologies.
  • Bachelor's degree in Computer Science, Engineering, or a related field.