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Remote RAG Engineering Jobs

Browse remote RAG positions — retrieval-augmented generation pipeline development, vector search engineering, and document AI roles at companies building knowledge-intensive LLM products.

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Remote worldwide
All job types
RAG Engineer LLM Retrieval Engineer Knowledge Base Engineer Conversational AI Engineer Document Intelligence Engineer

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Jobs & Projects

Perplexity

Posted 2 months ago

Internship - Machine Learning Research Engineer

Perplexity

Berlin

intern
Full-time
English
Open job
Replit

Posted 2 months ago

AI Agent Security Architect

Replit

Foster City, CA

Full-time
English
Open job
Cohere
Full-time
English
C$200K – C$245K / year
Open job
UiPath

Posted 2 months ago

Technical Account Manager

UiPath

Remote-Sao Paulo

Full-time
English
aws azure gcp rag machine learning +4 more
Open job
Tavily

Posted 2 months ago

Senior Software Engineer (Agentic Search) – Web Rendering (Chromium)

Tavily

Israel; Netherlands; Remote - Europe; United Kingdom

senior
Full-time
English
Open job
UiPath

Posted 2 months ago

Senior Technical Account Manager - Federal

UiPath

Remote-Virginia

senior
Full-time
English
Open job
LangChain

Posted 2 months ago

Deployed Architect, Professional Services (Amsterdam)

LangChain

Amsterdam

Full-time
English
Open job
LangChain

Posted 2 months ago

Deployed Architect, Professional Services (London)

LangChain

London

Full-time
English
Open job
LangChain

Posted 2 months ago

Deployed Architect, Professional Services (Austin)

LangChain

Austin, TX

Full-time
English
Open job
LangChain

Posted 2 months ago

Deployed Architect, Professional Services (Dallas)

LangChain

Dallas, TX

Full-time
English
Open job

Role guide

What to expect in remote RAG jobs

RAG (Retrieval-Augmented Generation) has become one of the most in-demand specialisations in AI engineering. Remote RAG jobs involve designing retrieval pipelines that combine vector search with LLM generation — reducing hallucinations and grounding responses in verified data. Engineers who master chunking, re-ranking, and retrieval evaluation are consistently in demand.

What you'll work on

  • Design end-to-end RAG pipelines for enterprise knowledge base Q&A systems
  • Optimise chunking strategies and embedding models for retrieval accuracy
  • Implement re-ranking and hybrid search to improve RAG response quality
  • Build evaluation pipelines that measure faithfulness, relevance, and groundedness

What companies look for

  • End-to-end RAG pipeline design — ingestion, chunking, embedding, retrieval, generation
  • Vector store integration (Pinecone, Weaviate, pgvector, Chroma)
  • RAG evaluation frameworks — RAGAS, TruLens, or custom scoring pipelines

Frequently asked questions

What remote RAG engineering jobs are available?

RAG engineer roles, LLM retrieval specialists, and document AI developers. Most require Python, LangChain or LlamaIndex experience, and familiarity with at least one vector database.

Is RAG experience enough for LLM engineering jobs?

RAG is a strong specialisation, but most companies also expect broader LLM engineering skills — prompt design, evaluation, API integration, and Python production engineering alongside RAG expertise.

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