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

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Jobs & Projects

Cresta

Posted 1 month ago

Staff Software Engineer, Backend

Cresta

Canada (Remote)

staff
Full-time
English
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Cresta

Posted 1 month ago

Staff Machine Learning Engineer

Cresta

United States (Remote)

staff
Full-time
English
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Cresta

Posted 1 month ago

Senior Software Engineer, Backend (AI Agent Runtime)

Cresta

Canada (Remote)

senior
Full-time
English
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Cresta
senior
Full-time
English
Open job
Cresta

Posted 1 month ago

Senior Developer Relations Engineer

Cresta

United States (Remote)

senior
Full-time
English
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Cresta

Posted 1 month ago

Lead Engineer - AI Agent Voice Experience

Cresta

United States (Remote)

lead
Full-time
English
rag ai agents machine learning machine learning vertex ai +2 more
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Cresta

Posted 1 month ago

Senior Machine Learning Engineer

Cresta

United States (Remote) · also open in 1 other location

senior
Full-time
English
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Cresta

Posted 1 month ago

Senior Forward Deployed Engineer (AI Agent) - UK

Cresta

United Kingdom (Remote)

senior
Full-time
English
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Notion

Posted 1 month ago

Forward Deployed Engineer, India

Notion

Hyderabad, India

Full-time
English
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Cohere
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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