Hire senior RAG engineers who make retrieval actually work
Senior engineers who build retrieval-augmented generation systems that find the right passage, respect access controls and prove it with evals.
By the Ryz Labs team · Updated October 2026
Hiring RAG engineers through Ryz gets you senior Latin American engineers who build retrieval-augmented generation systems that answer from your data, cite their sources and respect who is allowed to see what. They are the top 1% of the engineers we interview, and they work on your team and in your repos within an hour of US time zones.
What our RAG engineers work on
Most RAG failures are retrieval failures. The model answers confidently from the wrong passage, or the right document never makes it into the context. Our RAG engineers treat ingestion, search and ranking as the core of the work, and the prompt as the last step. Typical projects:
- Internal knowledge assistants over wikis, tickets and policies, with citations and links to the source.
- Customer-facing answer engines over product documentation, with fallbacks to a human when confidence is low.
- Permission-aware retrieval that mirrors SharePoint, Google Drive or database access controls at query time.
- Hybrid search pipelines combining BM25 keyword search, dense vectors and a cross-encoder reranker.
- Agentic retrieval where the model plans queries, searches several sources and decides when it has enough.
- Rebuilding a stalled RAG pilot: diagnosing whether ingestion, chunking, search or generation is the weak link.
Skills we vet for
- Ingestion and parsing. PDFs, HTML, tables and scanned documents, with deduplication and change detection for incremental updates.
- Chunking. Structure-aware chunking by heading or section, overlap, parent-child retrieval and contextual chunk headers.
- Embeddings. Choosing and benchmarking embedding models, dimensions, multilingual needs and re-embedding costs.
- Hybrid search and reranking. BM25 plus vectors, reciprocal rank fusion and cross-encoder or API rerankers.
- Query transformation. Query rewriting, decomposition and HyDE-style approaches, and when they help.
- Access control. Filtering by user and group at retrieval time, not after generation.
- Evaluation. Recall@k, MRR and nDCG for retrieval; faithfulness and answer relevance for generation, with labeled sets.
- Long context versus retrieval. Knowing when to put the whole document in context instead of retrieving pieces.
How we vet RAG engineers
Recruiters source engineers who have shipped retrieval systems to real users, then our in-house ARC system ranks the pipeline. Structured NTRVSTA AI interviews focus on retrieval design and failure diagnosis. Recruiters review each candidate before and after the interview and assemble a curated shortlist. AI scores are advisory; people decide.
Sample interview topics
- Users say the assistant "ignores" the newest policy. Walk through how you find out whether it is an ingestion, indexing, ranking or prompting problem.
- Product codes like "XR-2207B" never match in vector search. How do you fix retrieval for exact identifiers?
- Design permission-aware retrieval over 2 million documents where access changes daily.
- Build an evaluation set for a RAG system that has no labeled data. Where do the questions come from, and how do you avoid testing only easy cases?
- Your reranker improves accuracy but adds 600 ms. How do you decide whether to keep it?
Ways to hire RAG engineers
| Option | Best for | Trade-offs |
|---|
| Freelance marketplace | A chat-with-your-docs demo | Demos are easy. Getting retrieval accurate on your messy data is the real job, and few profiles show it. |
| Staffing or recruiting agency | Finding AI engineer candidates | Screens seldom test retrieval metrics or permission design. |
| In-house recruiting | A long-term search and knowledge team | Slow to staff, and the work often stalls while the role is open. |
| Ryz Labs staff augmentation | Adding retrieval expertise to a team that owns the product | You set priorities and review code. Best when data sources are accessible. |
| Ryz Labs AI pod team | Building a full RAG system, from ingestion to evals, in your cloud | A dedicated pod with a tech lead, ML and backend engineers and weekly demos. Larger scope, planned up front. |
Ryz is not the right fit if you want a hosted enterprise search product rather than engineers who build in your stack, or hourly gig work without a conversation. For European or Asian time-zone coverage, use a global network.
Why hire RAG engineers from Latin America
RAG quality depends on subject-matter experts telling engineers which answers are wrong and why. When your RAG engineers work your hours, they can sit with a support lead or analyst, go through failed questions one by one and turn each into a test case the same day.
Search and data engineering have deep roots in Latin America's engineering community, built over years of work for US product companies. RAG engineers with that background think about indexes, freshness and query latency, not just prompts. They work in English with your domain experts.
Related roles
FAQ
With long context windows, do we still need RAG?
Often, yes. Long context works well for a handful of documents, but large or frequently changing collections, per-user permissions and cost all favor retrieval. Our engineers test both on your data.
Can you fix a RAG pilot that stalled?
Yes. We start by building an evaluation set from real questions, then measure each stage to find where answers go wrong before changing anything.
How is pricing set?
Custom quote, scoped per team. You get a plan, a price and the names of the people who would do the work before you sign.
Do their hours overlap with ours?
Yes. They work within an hour of US time zones, including New York hours. Ryz engineers work on your team, reporting to your leads. Talk to us to scope your team.
Questions we didn't answer? Email info@ryzlabs.com.