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Remote Retrieval Augmented Generation Jobs (NOW HIRING)

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 ...

TEKsystems

Principal AI Engineer

Raleigh, NC · On-site +1

$75 - $100/hr

Seeking highly skilled AI Engineers with expertise in Multi-Agent Retrieval-Augmented Generation ... remote position. Application Deadline This position is anticipated to close on Jun 12, 2026. About ...

rSTAR Technologies LLC.

AI Architect

Westmont, IL · Remote

$63.50 - $82.75/hr

The ideal candidate will bring deep expertise in Python, FastAPI, and Retrieval-Augmented Generation (RAG) solutions, with hands-on experience deploying scalable AI applications on Azure. This role ...

UNFI

AI Automation Lead - Remote

Providence, RI · On-site +1

... retrieval-augmented generation (RAG), function calling, agent loops, and workflow orchestration ... Remote Role: * This position is classified as remote where the associate will perform remote work ...

Tebra

Senior AI Automation Engineer

OR · Remote

$103K - $136K/yr

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

Genstar Capital LLC

Experience with embeddings and retrieval-augmented generation (RAG) Preferred Qualifications * PhD in Machine Learning, Computer Science, or a related quantitative discipline. * Previous experience ...

SumerSports LLC

AI Application Engineer

Strong understanding of prompt engineering, retrieval-augmented generation, and evaluation ... Remote working environment * A flexible, unlimited time off policy * Generous paid holiday schedule ...

Blockskye

Senior Full-Stack Engineer (Node.js, AI)

Design and ship AI-native features end-to-end - LLM endpoints, retrieval-augmented generation (RAG) pipelines, tool/function calling, and agentic workflows - that meet the same bar for reliability ...

Mastech Digital

AI Full Stack Engineer

Chicago, IL · Remote

$70 - $75/hr

AWS Solutions Architect, AWS Developer, or Kubernetes Application Developer. - Hands-on experience with Langchain/LlamaIndex and RAG (retrieval-augmented generation) architecture. - Proven experience ...

Waymo

Senior Software Engineer, DevAI

San Francisco, CA · On-site +1

$213K - $263K/yr

... Retrieval-Augmented Generation (RAG) * Coding Excellence: Proficiency in architecting and ... remote, the specific salary range for your preferred location, during the hiring process. Waymo ...

Waymo

Senior Software Engineer, DevAI

Mountain View, CA · On-site +1

$213K - $263K/yr

... Retrieval-Augmented Generation (RAG) * Coding Excellence: Proficiency in architecting and ... remote, the specific salary range for your preferred location, during the hiring process. Waymo ...

Waymo

Senior Software Engineer, DevAI

San Francisco, CA · On-site +1

$213K - $263K/yr

... Retrieval-Augmented Generation (RAG) * Coding Excellence: Proficiency in architecting and ... remote, the specific salary range for your preferred location, during the hiring process. Waymo ...

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Remote Retrieval Augmented Generation information

What are the key skills and qualifications needed to thrive as a Remote Retrieval Augmented Generation Engineer, and why are they important?

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 some 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.
More about Remote Retrieval Augmented Generation jobs
What cities are hiring for Remote Retrieval Augmented Generation jobs? Cities with the most Remote Retrieval Augmented Generation job openings:
What are the most commonly searched types of Retrieval Augmented Generation jobs? The most popular types of Retrieval Augmented Generation jobs are:
What states have the most Remote Retrieval Augmented Generation jobs? States with the most job openings for Remote Retrieval Augmented Generation jobs include:
What job categories do people searching Remote Retrieval Augmented Generation jobs look for? The top searched job categories for Remote Retrieval Augmented Generation jobs are:
Remote Retrieval Augmented Generation jobs near you
Infographic showing various Remote Retrieval Augmented Generation job openings in the United States as of May 2026, with employment types broken down into 79% Full Time, and 21% Contract. Highlights an 100% Remote job distribution.

AI Engineer

CoreStory

Remote

Full-time

Posted 18 days ago

Job description

About CoreStory
CoreStory unlocks the hidden intelligence in your legacy code. By using AI to surface business logic and technical insights, we give enterprises the clarity to modernize faster, maintain apps smarter, and reduce the risk of costly failures.
We're looking for an AI Engineer who is passionate about building intelligent systems that blend large language models, retrieval architectures, and conversational agents into cohesive, scalable products. This role is critical to the core AI engine powering the CoreStory Platform.
Role Overview
As an AI Engineer, you'll play a central role in developing and optimizing the AI components that power CoreStory's narrative intelligence platform. You'll work across LLM integration, vector search systems, prompt orchestration, agentic systems, and retrieval-augmented generation (RAG) pipelines.
You'll collaborate closely with the product, data, and infrastructure teams to prototype, productionize, and continuously evolve our AI stack - ensuring that our systems are accurate, explainable, efficient, and on the cutting edge of modern AI capabilities.
Key Responsibilities
  • Design, implement, and optimize LLM-powered systems (e.g., RAG, chat agents, summarizers, knowledge graph integration).
  • Build and manage data indexing and retrieval pipelines using LlamaIndex, LangChain, or similar frameworks.
  • Implement and maintain vector databases (e.g., Pinecone, Neo4j, Weaviate, Chroma, or Azure Cognitive Search).
  • Integrate open-source and proprietary LLMs (e.g., GPT, Claude, Llama) into the CoreStory Platform.
  • Develop and refine AI-driven features - including generative insights, automated summarization, and narrative analytics.
  • Collaborate with DevOps and backend teams to deploy scalable AI services within CoreStory's cloud infrastructure.
  • Continuously benchmark model performance, latency, and cost, identifying opportunities for optimization.
  • Stay current with advancements in AI - from model architectures to emerging frameworks - and propose innovative applications aligned with CoreStory's mission.
  • Contribute to internal documentation, experimentation frameworks, and evaluation methodologies.
Qualifications
Required Skills:
  • 7+ years of overall engineering experience with at least 3+ years of experience in AI engineering, machine learning, or applied NLP.
  • Strong hands-on experience with LlamaIndex, LangChain, or similar orchestration frameworks.
  • Experience designing and implementing vector database solutions (e.g., Pinecone, Neo4j, FAISS, Milvus, Weaviate).
  • Solid understanding of LLM APIs (OpenAI, Anthropic, Mistral, Hugging Face, etc.).
  • Proficiency in Python, with experience in libraries such as FastAPI, Pandas, or NumPy.
  • Understanding of retrieval-augmented generation (RAG) patterns, embeddings, and tokenization.
  • Familiarity with prompt engineering, tool calling, and chat agent architectures.
  • Strong problem-solving and analytical mindset, with attention to performance and scalability.
  • Demonstrated interest in staying up-to-date with the fast-evolving AI landscape.

Preferred:
  • Experience deploying AI services in production (e.g., using Docker, Azure, or AWS).
  • Exposure to LangGraph, semantic search, or hybrid RAG systems.
  • Familiarity with knowledge graphs, document intelligence, or multimodal AI.
  • Previous experience in SaaS or early-stage startup environments.
What We Offer
  • Competitive compensation and equity.
  • Flexible, remote-first work environment.
  • Opportunities to define and build the AI roadmap of a fast-growing technology company.
  • Collaborative, learning-oriented culture.
  • Access to cutting-edge AI models, research, and infrastructure.

Apply