Skill specialists
Hire Vetted RAG Engineers
Find engineers who design and build retrieval-augmented generation systems — from vector indexing and chunking strategy to LLM integration, re-ranking, and production evaluation.
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Uriel
Khandu
Tameeem
Umair
Nada
Mohsin
Yash
Timoth
Olisaemeka
Marshaniswah
Hiring guide
Why companies hire RAG experts
RAG (Retrieval-Augmented Generation) is the architecture behind most production AI assistants that answer questions from company knowledge bases, documents, and real-time data. Skilled RAG engineers design the entire pipeline: chunking, embedding, retrieval, re-ranking, and response generation.
Popular projects
- Build an AI assistant that answers from your internal documents
- Create a customer-facing support bot trained on your product docs
- Develop a contract or legal document analysis system
- Build a private knowledge base AI over confidential company data
What to look for
- Deep understanding of chunking strategies and embedding models
- Experience with vector database selection and optimisation
- Knowledge of RAG evaluation (faithfulness, relevance, groundedness)
Frequently asked questions
What is a RAG engineer and what do they build?
A RAG engineer builds AI systems that retrieve relevant information from a knowledge base and use it to generate accurate, grounded responses. They design the full pipeline: document processing, embedding, vector storage, retrieval, and LLM integration.
What tools do RAG engineers typically use?
Common tools include LangChain or LlamaIndex for orchestration, OpenAI or open-source embedding models, vector databases (Pinecone, Weaviate, pgvector, Qdrant), and evaluation frameworks like RAGAS.
How long does it take to build a RAG system?
A simple prototype can be built in days. A production-ready RAG system with robust retrieval, evaluation, and monitoring typically takes 4-12 weeks depending on data complexity and quality requirements.
What is the difference between RAG and fine-tuning?
RAG retrieves relevant context at query time and feeds it to the LLM — no model weights change. Fine-tuning changes the model itself. RAG is better when your knowledge base changes frequently. Fine-tuning is better for teaching the model a specific tone, format, or domain vocabulary.
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