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Remote Retrieval Augmented Generation Jobs (NOW HIRING)

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 ...

Delan Associates, Inc

AI Agentic Tester

Denver, MO · Remote

AI Agentic Tester Denver, MO (Remote) Must-Have Skills AI Agentic Testing Functional Testing Generative AI Testing Large Language Models (LLMs) AI Agents & Agentic AI Retrieval-Augmented Generation ...

Delan Associates, Inc.

AI Agentic Tester

Denver, MO · On-site +1

... Remote) Must-Have Skills * AI Agentic Testing * Functional Testing * Generative AI Testing * Large Language Models (LLMs) * AI Agents & Agentic AI * Retrieval-Augmented Generation (RAG) * Prompt ...

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 ...

Accelint

Machine Learning Engineer

You will work with transformer models, retrieval-augmented generation (RAG), and advanced document processing techniques to enable search, question answering, summarization, and information ...

System One

Salesforce Developer

Washington, VA · Remote

$56.75 - $75.25/hr

Job Title: Salesforce Developer REMOTE Responsibilities * Develop Lightning components in ... Utilize AI-driven automation tools such as Einstein AI and retrieval-augmented generation (RAG) to ...

New

Tebra

Senior AI Automation Engineer

OR · Remote

$103K - $136K/yr

Own the Retrieval-Augmented Generation (RAG) lifecycle end-to-end, including ingestion, chunking ... Remote

Global Alliant Inc

AI Engineer

Rockville, MD · On-site +1

Remote/Hybrid (subject to contract requirements) Clearance: Must be eligible to obtain and maintain ... Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG)

Global Alliant Inc

AI Engineer

Rockville, MD · Remote

$140K/yr

Remote/Hybrid (subject to contract requirements) Clearance: Must be eligible to obtain and maintain ... Large Language Models (LLMs), Prompt Engineering, and Retrieval-Augmented Generation (RAG)

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Remote Retrieval Augmented Generation information

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 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.

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 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.
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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 68% Full Time, 30% Part Time, and 2% Contract. Highlights an 66% Physical, 3% Hybrid, and 31% Remote job distribution.

Engineer IV - Applied AI

PODS Enterprises, LLC

Clearwater, FL • Remote

Full-time

Posted 23 days ago

PODS rating

6.3

Company rating: 6.3 out of 10

Based on 29 frontline employees who took The Breakroom Quiz

10th of 29 rated removal and storage companies

Job description

JOB SUMMARY

Responsible for designing, developing, implementing, and supporting AI-powered solutions that address business challenges and create operational efficiencies across the organization. Leverages existing large language models (LLMs), retrieval-augmented generation (RAG) architectures, agentic workflows, data platforms, and software engineering practices to develop scalable solutions that enhance business processes, customer experiences, and decision-making capabilities. Partners closely with business stakeholders, product teams, and technology teams to identify opportunities, translate business needs into technical solutions, and deliver production-ready AI applications and services.

ESSENTIAL DUTIES AND RESPONSIBILITIES

AI Solution Design & Development

• Design, develop, implement, and support AI-powered applications and services utilizing large language models (LLMs), agentic workflows, prompt engineering techniques, APIs, and retrieval-augmented generation (RAG) architectures.

• Evaluate business requirements and translate complex or ambiguous business problems into scalable technical solutions.

• Research, evaluate, prototype, and recommend AI technologies, tools, platforms, and vendors to support business objectives.

• Design and develop conversational AI solutions, chatbots, virtual assistants, and intelligent workflow automation capabilities.

Data Integration & Engineering

• Develop and maintain integrations between AI solutions, enterprise applications, APIs, databases, and cloud data platforms.

• Query, transform, structure, and manage data to support AI and machine learning solutions utilizing platforms such as Snowflake and related technologies.

• Develop and maintain retrieval pipelines, semantic search capabilities, vector databases, and data services supporting AI-enabled solutions.

• Ensure data quality, security, governance, and performance requirements are met within assigned solutions.

Solution Delivery & Operational Support

• Deploy, monitor, maintain, and optimize AI solutions in production environments.

• Monitor application performance, reliability, utilization, and operating costs and implement improvements as appropriate.

• Troubleshoot application, integration, and data issues and provide timely resolution to support business operations.

• Create and maintain technical documentation, solution architecture diagrams, standards, and support materials.

Business Partnership & Innovation

• Partner with business stakeholders, product teams, and technology teams to identify opportunities for AI adoption and process automation.

• Provide technical leadership and subject matter expertise related to applied AI technologies and emerging industry trends.

• Support pilot programs, proofs of concept, and innovation initiatives that advance organizational capabilities.

• Promote adoption of AI-enabled solutions through training, knowledge transfer, and stakeholder engagement.

Continuous Improvement

• Stay current with developments in artificial intelligence, machine learning, software engineering, and data technologies.

• Continuously evaluate solution effectiveness and recommend enhancements to improve business value, quality, scalability, reliability, and user experience.

• Participate in architecture reviews, code reviews, and development best practices to ensure high-quality solution delivery.

MANAGEMENT & SUPERVISORY RESPONSIBILTIES

• Typically reports to an Engineering Manager, Director of Engineering, Director of AI, or other technology leadership position.

• Functions as a senior-level individual contributor.

• May provide technical leadership, mentoring, and guidance to engineers, analysts, and project teams.

• No direct supervisory responsibility.

JOB QUALIFICATIONS: Education & Experience Requirements

Education

• Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Information Technology, or related field required.

• Master's degree preferred.

• Equivalent combination of education, training, and experience may be considered.

Experience

• Minimum of 7 years of progressive software engineering, data engineering, artificial intelligence, machine learning, or related technology experience.

• Minimum of 3 years of experience designing, building, and deploying AI-enabled solutions within production environments.

• Experience developing applications using LLMs, AI APIs, prompt engineering, retrieval-augmented generation (RAG), and agentic workflow technologies.

• Experience with Python development, APIs, integrations, and cloud-based technologies.

• Experience working with Snowflake, SQL, data transformation, and enterprise data platforms.

• Experience implementing and supporting AI applications using modern frameworks and orchestration tools.

• Strong analytical, problem-solving, organizational, and communication skills.

• Experience leading technical initiatives and influencing cross-functional teams preferred.

What PODS employees say

Pay

Benefits

Hours and flexibility

Workplace

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