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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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Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows,...
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At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are en...
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Fireworks is the platform for specialized intelligence, enabling companies to build, train, and serve AI models tailored to their own data, workflows,...
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Posted 3 weeks ago
Senior AI/ML Architect, Applied Field Engineering/Field CTO
Snowflake
SG-Singapore
At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are en...
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Posted 3 weeks ago
Staff Software Engineer, Cortex AI Search Infrastructure
Snowflake
US-CA-Menlo Park
At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are en...
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Posted 3 weeks ago
Senior Software Engineer - Integrations - AI/ML
ClickHouse
Canada · also open in 4 other locations
The Connectors team is the bridge between ClickHouse and the broader ecosystem. We build and maintain the integrations that make ClickHouse accessible...
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Posted 3 weeks ago
Startup Business Development Lead - Snowflake for Startups
Snowflake
US-CA-Menlo Park
At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are en...
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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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