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Remote Retrieval Augmented Generation Jobs (NOW HIRING)

Inficare Technologies

Java Full Stack Lead

$53.75 - $69.25/hr

RAG (Retrieval-Augmented Generation) * Vector Databases * MCP Location & Work Model Fully remote - open to candidates across the United States. Engagement Details Contract engagement with an initial ...

Noblesoft Technologies

AI Developer

San Jose, CA · Remote

Remote JD: Core Responsibilities * Design, develop, and deploy autonomous AI agents using ... Integrate memory systems and RAG (Retrieval-Augmented Generation) using vector databases for ...

UnitedHealth Group

Sr AI/ML Engineer - Remote

Minnetonka, MN · On-site +1

$106K - $146K/yr

Design and implement retrieval-augmented generation (RAG) pipelines that integrate enterprise knowledge sources, vector search, embeddings, and LLM orchestration patterns * Support agentic AI ...

System One

GENAI CCAS Application Developer

Washington, DC · Remote

$56.23 - $62/hr

Remote (Quarterly Travel to Gaithersburg, MD) Hours: 40.0 Security Clearance: Ability to obtain a ... Build retrieval-augmented generation (RAG) pipelines to enhance Amazon Connect agent assist and ...

Inficare Technologies

Java Full Stack Engineer

$53.75 - $69.25/hr

RAG (Retrieval-Augmented Generation) * Vector Databases * MCP Location & Work Model Remote - USA (location TBC) Engagement Details Contract engagement. Exact duration to be confirmed. Immediate ...

Cresta

Senior Machine Learning Engineer

$125K - $165K/yr

Architect and scale LLM and retrieval-augmented generation pipelines that ground models in ... Remote work setup budget to help you create a productive home office * Monthly wellness and ...

CoreStory

AI Engineer

Understanding of retrieval-augmented generation (RAG) patterns, embeddings, and tokenization ... Flexible, remote-first work environment. * Opportunities to define and build the AI roadmap of a ...

NewRocket

Agentic AI Architect-Anthropic-US East

$64.50 - $85/hr

Our teams apply Anthropic-aligned practices in prompt and context engineering, retrieval-augmented generation (RAG), tool use, structured outputs, model evaluation, safety, governance, and human-in ...

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Remote Retrieval Augmented Generation information

What is remote retrieval augmented generation?

Remote Retrieval Augmented Generation (RAG) is an advanced AI technique that combines large language models with external information sources. In a remote RAG setup, the model retrieves relevant data from remote databases or APIs during the generation process, enhancing its responses with up-to-date or domain-specific knowledge. This approach is widely used in applications that require accurate, context-aware answers, such as chatbots, search engines, and virtual assistants. By leveraging remote retrieval, RAG systems can access a broader range of information without needing to store all data locally.

What skills and qualifications are needed to thrive as a remote retrieval augmented generation engineer?

To thrive as a Remote Retrieval Augmented Generation (RAG) Engineer, you need a strong background in machine learning, natural language processing, and information retrieval, often backed by a degree in computer science or a related field. Familiarity with tools and frameworks like PyTorch, TensorFlow, Hugging Face Transformers, and experience with retrieval systems such as Elasticsearch or FAISS are typically required. Problem-solving, effective communication, and adaptability are important soft skills for collaborating remotely and iterating on rapidly evolving AI solutions. These skills ensure the engineer can design, deploy, and optimize robust RAG systems that effectively combine retrieval and generation for high-quality AI outputs.

What are common challenges faced by professionals working in remote retrieval augmented generation roles, and how can they be addressed?

Professionals in Remote Retrieval Augmented Generation (RAG) roles often encounter challenges related to integrating diverse data sources, ensuring low latency in information retrieval, and maintaining the quality and relevance of augmented outputs. Coordinating effectively with distributed teams and adapting to rapidly evolving AI technologies are also common hurdles. To address these, staying current with best practices in data engineering, leveraging robust APIs, and participating in regular team check-ins can help ensure smooth collaboration and system performance.

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

AspectRemote Retrieval Augmented GenerationRemote Data Scientist
CredentialsAI/ML knowledge, programming skillsStatistics, programming, domain expertise
Work EnvironmentAI development, NLP projectsData analysis, model building
Industry UsageAI, NLP, machine learningTech, finance, healthcare
Search & ComparisonOften compared for AI roles involving language modelsCompared for data analysis roles

Remote Retrieval Augmented Generation focuses on developing AI models that combine retrieval techniques with language generation, requiring expertise in AI, NLP, and programming. Remote Data Scientists analyze data, build models, and interpret results, often with statistical and domain knowledge. While both roles may work remotely and involve data handling, Retrieval Augmented Generation emphasizes AI model development, whereas Data Scientists focus on data analysis and insights.

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Remote Retrieval Augmented Generation jobs near you
Infographic showing various Remote Retrieval Augmented Generation job openings in the United States as of August 2026, with employment types broken down into 67% Full Time, 32% Part Time, and 1% Contract. Highlights an 65% Physical, 2% Hybrid, and 33% Remote job distribution.

GCP / AI Cloud Engineer

INFOTRON

Sunnyvale, CA • Remote

$60K - $120K/yr

Full-time

Posted 7 days ago

Job description

Location: United Stated (Remote)
Job Type: [Full-Time / Contract]
Experience: 1–3 years
Clearance: Active U.S. Secret Security Clearance Required
About the Role
We are seeking a GCP AI / Cloud Engineer with hands-on experience in Google Cloud Platform, Infrastructure as Code, and Generative AI technologies. The ideal candidate will have experience building and deploying cloud-based solutions using GCP, Terraform, Vertex AI, RAG, and LLM technologies.
This role is suited for an early-career cloud/AI engineer who can work across cloud infrastructure and emerging AI workloads while following security and compliance requirements.
Key Responsibilities
  • Design, deploy, and maintain cloud infrastructure using Google Cloud Platform (GCP).
  • Develop and manage infrastructure using Terraform and Infrastructure as Code (IaC) practices.
  • Work with GCP-native services to build scalable, reliable, and secure cloud solutions.
  • Develop and support AI/ML and Generative AI applications using Vertex AI.
  • Build and integrate Retrieval-Augmented Generation (RAG) solutions.
  • Work with Large Language Models (LLMs) and contribute to LLM-powered applications and services.
  • Assist with cloud architecture, deployment, configuration, monitoring, and troubleshooting.
  • Implement cloud security and infrastructure best practices.
  • Collaborate with engineering and technical teams to develop and deploy cloud and AI solutions.
  • Troubleshoot infrastructure, application, and deployment issues across GCP environments.
  • Follow security, compliance, and operational requirements associated with classified environments.

Required Qualifications
  • 1-3 years of relevant professional experience in cloud engineering, DevOps, AI/ML engineering, or a related technical field
  • Hands-on experience with Google Cloud Platform (GCP)
  • Familiarity with Terraform and Infrastructure as Code (IaC)
  • Experience with GCP-native cloud services
  • Experience with Vertex AI
  • Experience with Retrieval-Augmented Generation (RAG) concepts and implementations
  • Experience with LLM development or Generative AI applications.
  • Active U.S. Secret Security Clearance
  • Google Cloud Associate Cloud Engineer certification
  • CompTIA Security+ certification required, or the ability and willingness to obtain it within 30 days

Preferred Qualifications
  • Experience deploying AI/ML workloads in GCP
  • Familiarity with cloud security principles and secure infrastructure practices
  • Experience with CI/CD and automated cloud deployments
  • Familiarity with containerization and Kubernetes
  • Experience working with APIs, Python, or other programming languages used for AI/cloud development
  • Experience with vector databases, embeddings, and document retrieval pipelines
  • Understanding of IAM, networking, logging, monitoring, and security controls within GCP

Required
  • Google Cloud Associate Cloud Engineer certification
  • Active U.S. Secret Security Clearance
  • CompTIA Security+, or ability to obtain within 30 days

What We're Looking For
We are looking for someone who combines cloud engineering fundamentals with practical Generative AI experience. You should be comfortable working with GCP infrastructure while also understanding how technologies such as Vertex AI, RAG, and LLMs are used to build production-oriented AI solutions.
Candidates must be authorized to work in the United States and must possess an active U.S. Secret Security Clearance.

This is a remote position.