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Overnight Retrieval Augmented Generation Jobs (NOW HIRING)

Develop LLM-powered applications leveraging Retrieval-Augmented Generation (RAG), tool calling, and orchestration frameworks. * Build scalable APIs, microservices, and integrations supporting ...

AI Engineer

Dallas, TX · On-site

$90 - $120/hr

Develop and maintain Retrieval-Augmented Generation (RAG) architectures using vector databases and semantic search technologies * Create, test, and refine prompts, structured outputs, and evaluation ...

Python AI Developer

Malvern, PA · On-site

$49.25 - $68/hr

Experience designing and implementing Retrieval-Augmented Generation (RAG) solutions * Hands-on experience with LangChain or similar AI orchestration frameworks * Experience with AWS services such as:

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

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

The ideal candidate will have hands-on experience building AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, prompt engineering, and enterprise AI ...

Showing results 21-40

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 August 2026, with employment types broken down into 65% Full Time, 33% Part Time, and 2% Contract. Highlights an 63% Physical, 3% Hybrid, and 34% Remote job distribution.

Agentic AI Engineer

Oraapps Inc

Atlanta, GA • On-site

Other

Posted 8 days ago


Job description

What You''ll Do

  • Design, develop, and deploy enterprise-grade AI applications using modern software engineering best practices.
  • Build and enhance agentic AI solutions, AI copilots, and intelligent workflow automation.
  • Develop LLM-powered applications leveraging Retrieval-Augmented Generation (RAG), tool calling, and orchestration frameworks.
  • Build scalable APIs, microservices, and integrations supporting enterprise AI platforms.
  • Collaborate with data scientists to productionize machine learning and AI solutions.
  • Implement testing, monitoring, observability, and governance practices for AI applications.
  • Ensure AI solutions meet security, compliance, and responsible AI standards.
  • Contribute to architecture decisions for enterprise AI platforms and reusable application frameworks.
  • Work within Azure and Microsoft''s AI ecosystem while supporting multi-cloud best practices where appropriate.
  • Participate in code reviews and promote engineering excellence across the team.

Current AI Initiatives

  • This role will contribute to several strategic AI initiatives, including:
  • Payer Intelligence Platform
  • Monitor payer policy changes using AI.
  • Assess operational impact of policy updates.
  • Support managed care teams in prioritizing actions and dispute resolution.
    • Clinical Chart Review: Build agentic AI solutions using EHR and clinical documentation.
  • Support patient cohort identification.
  • Generate clinical insights for quality improvement initiatives.
  • Population Market Intelligence: Analyze internal and external datasets.
  • Generate recommendations for service line growth.
  • Identify emerging healthcare market opportunities.

Required Qualifications

  • MUST HAVE A Bachelor''s degree in Computer Science, Engineering, Data Science, or a related technical field. Master''s degree preferred. Equivalent professional experience may be considered in lieu of an advanced degree.
  • Approximately 3+ years of experience in AI engineering, machine learning engineering, data engineering, software engineering, or a related technical discipline.
  • At least 2 years of experience designing, building, and supporting production-grade enterprise applications.
  • Hands-on experience developing applications using Large Language Models (LLMs).
  • Experience implementing Retrieval-Augmented Generation (RAG) architectures.
  • Experience building agentic AI applications, AI assistants, or workflow automation solutions.
  • Strong Python programming skills.
  • Experience building and consuming RESTful APIs.
  • Knowledge of software engineering best practices, including testing, version control, CI/CD, and maintainable application design.
  • Experience designing scalable enterprise application architectures.

Preferred Qualifications

  • Experience within healthcare, provider organizations, payer organizations, or biomedical environments.
  • Experience with Microsoft Azure and Azure AI services.
  • Familiarity with GitHub Copilot and the Microsoft AI ecosystem.
  • Experience with AI governance, responsible AI practices, observability, guardrails, and model monitoring.
  • Background in MLOps and production AI deployment.
  • Technical Environment
  • Python
  • Azure (preferred)
  • GitHub Copilot
  • Microsoft AI ecosystem
  • REST APIs
  • Microservices
  • Enterprise AI architecture
  • Tool-calling frameworks
  • Retrieval-Augmented Generation (RAG)