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Retrieval Augmented Generation Jobs (NOW HIRING)

Build and optimize Retrieval-Augmented Generation (RAG) solutions. Develop AI Agents and conversational AI systems. Integrate OpenAI, Azure OpenAI, Claude, Gemini, or Llama models into enterprise ...

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Implement RAG (Retrieval Augmented Generation) patterns using requirements, user stories, APIs, configurations, and test repositories, leverage embeddings and vector search where applicable. Apply ...

Closure Technologies is seeking a AI/ML Engineer who will Implement and maintain Retrieval-Augmented Generation (RAG) pipelines and integrate Large Language Models (LLMs) into applications, supported ...

Additionally, experience in building Retrieval-Augmented Generation (RAG) pipelines for search and chat applications is highly desired. Key Responsibilities: * Develop and optimize NLP models for ...

GPT, Claude • Prompt Engineering • RAG (Retrieval Augmented Generation) • AWS Cloud • Strong architectural and hands on GenAI expertise • Experience with enterprise automation and testing ...

The ideal candidate will have a strong background in investment banking, hands-on experience with Microsoft Azure OpenAI, and expertise in Retrieval-Augmented Generation (RAG). Key Responsibilities:

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Retrieval Augmented Generation information

What are the typical daily responsibilities of a Retrieval Augmented Generation engineer?

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 job?

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 are the key skills and qualifications needed to thrive in the Retrieval Augmented Generation position, and why are they important?

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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Infographic showing various Retrieval Augmented Generation job openings in the United States as of June 2026, with employment types broken down into 75% Full Time, and 25% Contract. Highlights an 75% In-person, and 25% Remote job distribution.

Python / Generative AI Engineer

Prophecy Technologies

Los Angeles, CA • On-site

Full-time

Posted 22 days ago


Job description

JOB SUMMARY
The Python / Generative AI Engineer will design, develop, and implement AI-driven applications leveraging Generative AI technologies. The role focuses on building scalable AI solutions using Python, developing Retrieval-Augmented Generation (RAG) systems, and integrating AI models within cloud environments. The engineer will work closely with data scientists, engineers, and DevOps teams to implement GenAI solutions, optimize prompts, and deploy AI applications using modern cloud and container technologies.
Location
Los Angeles, CA / Irvine, CA (Hybrid)
Experience
5+ Years
Key Responsibilities
• Design and develop scalable applications using Python and SQL.
• Implement Generative AI solutions leveraging modern AI frameworks and tools.
• Build and maintain Retrieval-Augmented Generation (RAG) systems for AI-powered applications.
• Develop prompt engineering strategies to improve GenAI model outputs.
• Integrate AI solutions with cloud platforms and enterprise systems.
• Deploy and manage AI workloads using AWS, Docker, and DevOps pipelines.
• Collaborate with cross-functional teams to translate business problems into AI-driven solutions.
• Optimize AI workflows and ensure performance, reliability, and scalability.
• Implement and manage the Generative AI lifecycle including development, testing, deployment, and monitoring.
• Troubleshoot and resolve issues related to AI model integration and deployment.
Required Skills & Experience
• Minimum 5+ years of strong hands-on experience in Python development.
• Strong proficiency in SQL for data processing and analysis.
• Hands-on experience in Generative AI development using Python.
• Experience building Retrieval-Augmented Generation (RAG) based AI systems.
• Strong knowledge of prompt engineering and GenAI model interaction.
• Experience with AWS cloud services.
• Experience with containerization technologies such as Docker.
• Familiarity with DevOps practices and CI/CD pipelines.
• Strong analytical thinking, problem-solving, and critical reasoning skills.
• Ability to work independently with strong ownership and accountability.
Competencies
• Python Development
• Generative AI Development
• Retrieval-Augmented Generation (RAG)
• Prompt Engineering
• Cloud Computing (AWS)
• DevOps & Containerization
• SQL & Data Processing
Preferred Skills
• Experience using LangChain for building AI agents and GenAI workflows.
• Experience designing enterprise-level AI applications.
• Exposure to AI/ML model lifecycle management and deployment frameworks.