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RAG (Retrieval-Augmented Generation) is how enterprise AI products give LLMs access to proprietary knowledge. New RAG engineers on RemoteAI are reviewed by our team before they go live; their profiles describe production RAG system design that goes beyond the basic tutorial into chunking strategies, evaluation frameworks, and latency-optimized retrieval at scale.
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RAG (Retrieval-Augmented Generation) is how enterprise AI products give LLMs access to proprietary knowledge. New RAG engineers on RemoteAI are reviewed by our team before they go live; their profiles describe production RAG system design that goes beyond the basic tutorial into chunking strategies, evaluation frameworks, and latency-optimized retrieval at scale.
RAG engineers design and build systems that combine vector search with LLM generation to answer questions over private documents, internal knowledge bases, and structured data. Their work covers document processing and chunking strategies, embedding model selection and fine-tuning, vector database setup (Pinecone, Weaviate, Qdrant, pgvector, Chroma), hybrid retrieval (dense + sparse), reranking, evaluation with RAGAS or custom frameworks, and production serving with latency optimization. They integrate LLM providers (OpenAI, Anthropic, Hugging Face) and observability via LangSmith or Traceloop.
Finding the right RAG engineer requires more than evaluating technical skills — it means identifying someone who ships production-ready work and understands your business goals. Here is exactly how to do it.
What documents should the system know about: PDFs, internal wikis, databases, support tickets, code? Document type and volume directly affect the chunking strategy, embedding model, and vector database choice.
The retrieval step is where most RAG systems fail. Ask how the engineer evaluates retrieval precision and recall, how they handle multi-hop questions, and what reranking strategies they have used.
A RAG system without systematic evaluation is not production-ready. Ask about their approach to RAGAS metrics (faithfulness, relevancy, context precision) or custom evaluation approaches.
Testing retrieval quality on 50–100 representative questions from your domain reveals chunking and indexing issues before you invest in the full generation pipeline.
RAG is better when knowledge changes frequently, when you need citations, or when training data is limited. Fine-tuning is better when you need to change model style, teach complex reasoning patterns, or when low-latency inference matters more than freshness. Most enterprise applications should start with RAG.
Senior engineers with production hybrid retrieval, evaluation frameworks and LLMOps experience charge more than mid-level RAG engineers, and a scoped RAG prototype costs much less than a production system. Rates depend on seniority, country and scope. Put a budget or rate range in your job post and ask candidates for their rate in the first message; RemoteAI takes no placement fee on what you pay them.
pgvector (Postgres extension) is the simplest starting point if you already use Postgres. Pinecone is managed and easy to scale. Weaviate and Qdrant are open-source with strong self-hosted options. Chroma is great for prototyping. A RAG engineer can recommend based on your scale and infrastructure.
Chunking is splitting documents into smaller pieces that fit in the context window and can be meaningfully embedded. Poor chunking is the most common cause of RAG quality failures: chunks that are too large dilute relevance, too small lose context. Good RAG engineers use semantic chunking, sentence splitting, or document-structure-aware approaches rather than naive fixed-size splits.
Modern vector databases scale to hundreds of millions of document chunks. Retrieval latency and embedding cost are the practical constraints. At large scale, approximate nearest neighbor search (HNSW, IVF) maintains millisecond retrieval times even over very large corpora.
