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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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Posted 4 months ago
Applications closedLontex Tech
Remote
Timezone not specified
Context windows are getting longer. 128K, 200K, 1M tokens. The naive conclusion is that chunking becomes less important — just put everything in conte...
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Posted 4 months ago
Applications closedEmrex AI
Remote
Chicago, United States (UTC-6)
RAG performance lives or dies on chunking strategy. Fixed-size character chunks miss sentence boundaries and break coherent thought units. Sentence-le...
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Posted 4 months ago
Applications closedInovex Intelligence
Remote
Los Angeles, United States (UTC-8)
Stack Overflow's 'how do I do X in Python' worked when codebases were small. When your codebase is 8 million lines of proprietary code, the question i...
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Posted 4 months ago
Applications closedScirvex Intelligence
Remote
New York, United States (UTC-5)
A thorough prior art search for a software patent application takes a patent attorney 6-15 hours and costs clients $3,000-$8,000. Our platform reduces...
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Posted 4 months ago
Applications closedEmrex AI
Remote
New York, United States (UTC-5)
RAG systems fail quietly. The retrieval returns plausible-looking chunks that don't actually answer the question. The generation produces confident te...
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Posted 4 months ago
Applications closedTuvex Solutions
Remote
Chicago, United States (UTC-6)
Support teams spend 45 minutes per ticket searching through documentation that should take 90 seconds to find. We've built a conversational knowledge...
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Posted 4 months ago
Applications closedQuelion Cloud
Remote
Chicago, United States (UTC-6)
Search boxes are dying. Users want to ask questions, have conversations, and get answers that synthesize across multiple documents — not just a ranked...
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Posted 4 months ago
Applications closedScirvex Intelligence
Remote
New York, United States (UTC-5)
Academic researchers spend 15% of their time searching for grant opportunities and writing eligibility assessments. Most of that time is wasted on opp...
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Posted 4 months ago
Applications closedGaltion Intelligence
Remote
Chicago, United States (UTC-6)
We power the AI assistant behind 3 major airline customer support lines. When a passenger texts about a delayed flight, rebooking options, or a lost b...
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Posted 5 months ago
Applications closedLamion Solutions
Remote
Chicago, United States (UTC-6)
Clinical notes are the most valuable and least structured data in healthcare. Doctors write them in shorthand, with abbreviations, typos, and domain-s...
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Role guide
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 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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