Hire a RAG Architect
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Retrieval-augmented generation is how enterprises ground AI in their own data. RAG Architects design the retrieval pipelines, embedding strategies, and vector database infrastructure that determine whether an AI application returns accurate, cited answers or confident-sounding hallucinations.
What a RAG Architect does
RAG Architects design the system that retrieves relevant context from enterprise data sources and feeds it to the language model at inference time. This is the dominant pattern for enterprise AI applications because it allows organizations to ground AI responses in their own documents, databases, and knowledge bases without fine-tuning the underlying model.
The architecture involves multiple layers that each require distinct expertise: document ingestion and chunking (how to split source documents into segments that preserve semantic meaning), embedding generation (selecting and deploying the right embedding model for the domain), vector storage (choosing and configuring the database — Pinecone, pgvector, Azure AI Search, Weaviate — that holds the embeddings), retrieval pipeline design (combining semantic search with keyword search, metadata filtering, and reranking for precision), and orchestration (connecting the retrieval output to the LLM prompt in a way that maximizes answer quality).
The difference between a good RAG system and a mediocre one is enormous. A well-designed system returns the three most relevant document sections with high precision, leading to accurate, well-grounded answers. A poorly designed system returns tangentially related content, producing answers that sound authoritative but cite the wrong information — which is worse than no AI at all.
Why this role is hard to fill right now
RAG architecture requires expertise that spans information retrieval, NLP, database engineering, and application development. The field is evolving rapidly — retrieval strategies that were state-of-the-art six months ago are already being superseded by hybrid approaches, multi-stage retrieval, and agentic RAG patterns. Most candidates with RAG experience have built simple single-vector-store implementations. Architects who understand multi-modal retrieval, evaluation-driven chunk optimization, and production-scale indexing pipelines are rare.
How Xylity fills this role
We evaluate RAG Architects on the quality decisions they've made in past implementations: their chunking strategy rationale, how they measured retrieval precision, what reranking approach they used and why, and how they handled failure cases where the retrieval pipeline returned irrelevant results. We verify production experience by asking about indexing pipeline performance, latency targets they achieved, and how they handled document updates in a live system.
Where this role gets deployed
Enterprise knowledge base
Designing a RAG system that retrieves from SharePoint, Confluence, and internal wikis to power an employee-facing AI assistant.
Regulatory document Q&A
Building a retrieval system for legal, compliance, or regulatory documents where answer accuracy and citation are critical.
Multi-modal retrieval
Architecting RAG across text, tables, and images from PDF documents with hybrid search and metadata filtering.
What to look for when interviewing
These are the dimensions our consultants evaluate when screening RAG Architect candidates. Use them as a guide during your own interviews.
Chunking strategy
Can they explain why they chose their chunking approach and what alternatives they tested?
Retrieval evaluation
How do they measure retrieval quality beyond manual spot-checking?
Production performance
What latency targets have they achieved and how did they get there?
Failure handling
What happens when the retrieval pipeline returns nothing relevant?
Request RAG Architect Profiles
Tell us about your project context and timeline. We'll deliver 2–4 curated, pre-vetted profiles within 5 days of your initial brief.