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🎬 Intro
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A language model that can only answer questions from its training data is a limited tool. RAG — Retrieval Augmented Generation — is the architecture that makes LLMs genuinely useful for company-specific knowledge: answering questions about your documentation, contracts, or internal knowledge base without hallucinating. Post your RAG job on RemoteAI and hire an engineer who can build a retrieval system accurate enough to trust in production.
Pre-vetted talent pool
These are the types of RAG engineers available on RemoteAI — all pre-vetted, confirmed profiles.
The documents or data you are building retrieval over (PDFs, web pages, database records), the expected query patterns, and the LLM provider you are using.
Engineers who have designed chunking strategies, selected embedding models, and built evaluation frameworks for retrieval quality — not just those who followed a LangChain RAG tutorial.
Ask how candidates measure retrieval precision and handle documents that return high similarity scores but wrong answers. Evaluation design separates good RAG engineers from great ones.
RemoteAI takes nothing from your contract.
Hiring a rag engineer remotely starts with a clear, well-scoped job post. This guide covers what to include, how RemoteAI's matching works, and what you can expect at each step of the process.
RAG engineers on RemoteAI design and implement retrieval systems that give language models access to external knowledge at inference time. Core skills: document chunking strategy design (fixed-size, semantic, recursive), embedding model selection and fine-tuning (OpenAI, Cohere, BGE, E5), vector database setup and optimization (Pinecone, Weaviate, Qdrant, pgvector), hybrid search combining dense retrieval with BM25, and re-ranking pipeline design using cross-encoders. Strong RAG engineers understand evaluation — how to measure retrieval precision and generation faithfulness, not just whether the system returns an answer — and the failure modes: hallucination despite retrieval, context window exhaustion, and lexically similar but semantically irrelevant results.
A well-structured job post attracts the right rag engineers and filters out irrelevant applications. Here is the exact process to follow on RemoteAI.
The documents or data you are building retrieval over (PDFs, web pages, database records), the expected query patterns, and the LLM provider you are using.
Engineers who have designed chunking strategies, selected embedding models, and built evaluation frameworks for retrieval quality — not just those who followed a LangChain RAG tutorial.
Ask how candidates measure retrieval precision and handle documents that return high similarity scores but wrong answers. Evaluation design separates good RAG engineers from great ones.
RemoteAI takes nothing from your contract.
Looking to browse talent directly?
View vetted RAG Engineer Jobs and contact them directly — no job post needed.
The knowledge source (document type, volume, update frequency), embedding model preference, vector database choice, LLM provider, latency requirements, and whether evaluation framework setup is in scope.
Pinecone is fully managed and easiest to start with. Weaviate and Qdrant offer more control for self-hosted deployments. pgvector is ideal if your data already lives in PostgreSQL. The right choice depends on scale, existing infrastructure, and ops capacity — a RAG engineer on RemoteAI can recommend based on your specific context.
Free. No listing fee, no commission on contracts.
RAG engineers on RemoteAI typically charge $80–$180/hour. Hybrid search and evaluation pipeline specialists are at the higher end.
Create a free account and describe the knowledge source, query patterns, and LLM provider. RemoteAI matches your post to RAG engineers with retrieval architecture and evaluation experience. Most posts have a shortlist within 24 hours.