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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
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RAG Engineer LLM Retrieval Engineer Knowledge Base Engineer Conversational AI Engineer Document Intelligence Engineer

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Jobs & Projects

Brainco

Posted 2 days ago

Machine Learning Engineer, Applied AI Intern (Summer 2027)

Brainco

San Francisco Bay Area

intern
Full-time
English
machine learning pytorch machine learning llm jax +1 more
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Celonis

Posted 4 days ago

Lead Applied Value Engineer - AI & Process Intelligence Consultant

Celonis

Remote, United Arab Emirates

lead
Full-time
English
Open job
CodeRabbit

Posted 4 days ago

Senior Applied AI Engineer

CodeRabbit

San Francisco

senior
Full-time
English
$200K – $300K / year
reinforcement learning generative ai langchain llm typescript +5 more
Open job
Qventus

Posted 5 days ago

AI Tooling Engineer

Qventus

Remote, United States

Full-time
English
$120K – $150K / year
python langchain llm typescript rag +1 more
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Qventus

Posted 5 days ago

Forward Deployed AI Solutions Architect

Qventus

Remote, United States

Full-time
English
$220K – $245K / year
machine learning python generative ai prompt engineering a/b testing +5 more
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Dropzone AI

Posted 5 days ago

AI Research Engineer

Dropzone AI

Remote - US

Full-time
English
$200K – $250K / year
Open job
Snowflake
principal
Full-time
English
aws azure generative ai machine learning gcp +2 more
Open job
Cohere
Contract
English
rag machine learning llm ai training
Open job
Benchling

Posted 1 week ago

Software Engineer, Cloud Infrastructure

Benchling

San Francisco, CA

Full-time
English
$195K – $265K / year
python aws llm kubernetes aws bedrock +1 more
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Firecrawl

Posted 1 week ago

Senior Field Engagement Manager

Firecrawl

San Francisco, CA

senior
Full-time
English
$174K – $235K / year
javascript sql rag web scraping n8n
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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