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Overnight 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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Overnight Retrieval Augmented Generation information

What is the difference between Overnight Retrieval Augmented Generation vs Data Scientist?

AspectOvernight Retrieval Augmented GenerationData Scientist
CredentialsTypically requires knowledge of AI, NLP, and data retrieval techniquesRequires degrees in data science, statistics, or related fields
Work EnvironmentOften in AI research labs, tech companies, or startups focusing on NLP modelsIn corporate, research, or consulting settings analyzing data and building models
Industry UsagePrimarily in AI, machine learning, and NLP industriesAcross finance, healthcare, tech, and other sectors

Overnight Retrieval Augmented Generation focuses on developing AI models that combine retrieval techniques with generative AI, often working overnight to update or improve models. Data Scientists analyze data, build predictive models, and interpret results across various industries. While both roles involve data and AI, Retrieval Augmented Generation specialists focus on model training and NLP innovations, whereas Data Scientists handle broader data analysis and modeling tasks.

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Infographic showing various Overnight Retrieval Augmented Generation job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 32% Full Time, 60% Part Time, 2% Temporary, 4% Contract, and 1% Nights. Highlights an 65% Physical, 2% Hybrid, and 33% 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.