What RAG Engineers Build
RAG engineers design and implement systems that retrieve relevant information from your data and feed it to LLMs for accurate, grounded responses. This involves: document ingestion and chunking pipelines, embedding generation and vector storage, retrieval and re-ranking algorithms, prompt engineering for retrieval-augmented responses, and evaluation frameworks to measure answer quality.
Core Skills to Assess
Must-Have
Vector databases (Pinecone, Weaviate, ChromaDB, pgvector), embedding models and their tradeoffs, chunking strategies for different document types, retrieval evaluation metrics (recall, precision, MRR), Python and SQL proficiency, and experience with at least one LLM API provider.
Differentiators
Senior RAG engineers should also demonstrate: hybrid search combining vector and keyword retrieval, advanced re-ranking techniques, multi-modal RAG (text plus images, tables), query routing and decomposition, and experience optimizing for latency and cost at scale.
How to Evaluate RAG Engineers
Give candidates a practical exercise: provide a corpus of documents and ask them to build a RAG pipeline that answers specific questions. Evaluate their chunking decisions, retrieval quality, prompt design, and how they handle questions the corpus can't answer. The best candidates will also propose an evaluation framework without being asked.
Market Rates
US-based RAG engineers earn $140K-$200K annually. Latin American RAG engineers earn $5,000-$7,500/month. The role is well-suited for remote work — RAG development is primarily code, configuration, and evaluation work that doesn't require physical presence.
Hiring Through South
South has seen a significant increase in qualified RAG engineers across Latin America as the technology has matured. Our candidates come with production RAG experience, not just tutorial projects. We test for real-world skills like handling messy document formats, optimizing retrieval quality, and building evaluation pipelines.
