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Retrieval Augmented Generation Jobs in Georgia (NOW HIRING)

Cliff Services Inc

Qdrant Developer

Alpharetta, GA · On-site

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

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 ...

Virtual Networx

AI/ML Engineer

Atlanta, GA · On-site

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 ...

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 ...

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Retrieval Augmented Generation information

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 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 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.

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:
What are popular job titles related to Retrieval Augmented Generation jobs in Georgia? For Retrieval Augmented Generation jobs in Georgia, the most frequently searched job titles are:
What job categories do people searching Retrieval Augmented Generation jobs in Georgia look for? The top searched job categories for Retrieval Augmented Generation jobs in Georgia are:
What cities in Georgia are hiring for Retrieval Augmented Generation jobs? Cities in Georgia with the most Retrieval Augmented Generation job openings:
Retrieval Augmented Generation jobs near you
Infographic showing various Retrieval Augmented Generation job openings in Georgia as of August 2026, with employment types broken down into 72% Full Time, 26% Part Time, and 2% Contract. Highlights an 62% Physical, 3% Hybrid, and 35% Remote job distribution.

Qdrant Developer

Cliff Services Inc

Alpharetta, GA • On-site

Other

Posted 16 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.

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