To hire RAG developers who are actually good, screen for retrieval quality and evaluation, not for familiarity with a framework. Anyone can follow a tutorial that chunks a PDF, embeds it, stores it in a vector database and feeds the top five results to a model. That demo works on day one. The engineer you want is the one who can explain why it gives a confidently wrong answer on day thirty, and who built the test set that caught it.
Key Takeaways
- A RAG developer builds systems that fetch your own data and hand it to an LLM at question time. The hard part is retrieval: getting the right passage, not generating fluent text.
- Screen for evaluation habits first. A candidate who has never measured retrieval recall on a labeled question set has built demos, not products.
- Running costs are usually small next to salary. Embedding 10M tokens with OpenAI's text-embedding-3-small costs about $0.20 at current prices.
- Most teams do not need a "RAG specialist." They need a strong backend or AI engineer who has shipped one retrieval system to real users and owned its quality.
What a RAG Developer Actually Does
RAG stands for retrieval-augmented generation. The term comes from a 2020 paper by Lewis and colleagues at NeurIPS, which combined a pre-trained generator with a dense vector index searched by a neural retriever. The idea held up. The specific architecture in the paper did not matter much; the pattern did.
In a startup, the job breaks down roughly like this. About a fifth of it is plumbing: ingestion pipelines, embeddings, a vector store, an API. The rest is quality work. Chunking strategy. Hybrid search that mixes keyword and vector matches. Re-ranking. Metadata filters so a customer never sees another customer's documents. Prompt design that tells the model to say "I don't know." And an evaluation setup that turns "it feels better" into a number.
That last piece is where weak hires fall down. If you are also hiring for prompt and context design, read how to hire a context engineer; the roles overlap but are not the same.
The Skills to Screen For
| Skill | What good looks like | Red flag |
|---|---|---|
| Retrieval evaluation | Built a labeled question set, tracks recall@k and answer accuracy per release | "We tested it manually and it looked good" |
| Chunking | Chooses chunk size by document type, keeps headings and tables intact | One fixed size for everything because the tutorial did |
| Search | Has used hybrid (BM25 + vector) and a re-ranker, knows when each helps | Thinks a bigger embedding model fixes bad retrieval |
| Data security | Filters by tenant at query time, can explain prompt injection via documents | Puts every customer's data in one unfiltered index |
| Cost and latency | Knows token counts per request and caches where it can | Has never looked at the bill |
| Storage choice | Can defend pgvector vs a managed store for your scale | Picked a vendor because of a blog post |
On storage: pgvector adds vector search to Postgres with HNSW and IVFFlat indexes, which is plenty for most startups already on Postgres. Managed options like Pinecone and Weaviate make sense at larger scale or when you want someone else running it. A good candidate has an opinion here and can explain it in two minutes.
Interview Questions to Use When You Hire RAG Developers
Ask about something they shipped, then push on the parts tutorials skip:
1. "Your RAG bot answers a pricing question with last year's price. Walk me through how you find the cause." Good answers check retrieval first (was the old document retrieved, was the new one indexed), then the prompt.
2. "How did you know your last change improved quality?" You want a labeled eval set, a metric, and a before/after number. "Users liked it" is a weak answer.
3. "How do you chunk a 60-page contract versus a FAQ page?" Look for structure-aware splitting and overlap reasoning.
4. "How do you stop a user from seeing another tenant's documents?" The only acceptable answer filters at retrieval time, never in the prompt.
5. "A document in the index says 'ignore previous instructions.' What happens?" They should know about indirect prompt injection and have a mitigation.
A short paid exercise works well: give them 50 documents and 20 questions with known answers, and ask them to report retrieval accuracy before and after one improvement. Our guide on vetting an engineer's AI skills has more on structuring that.
A Concrete Version
A B2B support startup wants a help-center assistant over 4,000 articles, about 10M tokens of text. They expect 30,000 questions a month, each sending roughly 4,000 tokens of retrieved context and instructions and getting 500 tokens back.
Running costs, using official prices as of October 2026:
- Embedding the corpus once with OpenAI's text-embedding-3-small at $0.02 per million tokens: 10 x $0.02 = $0.20.
- Generation on Claude Sonnet 5.5 at $2 input / $10 output per million tokens: 30,000 x 4,000 = 120M input tokens = $240; 30,000 x 500 = 15M output tokens = $150. Total about $390 a month.
- The same traffic on Claude Haiku 4.5 at $1 / $5: $120 + $75 = $195 a month.
- Vector storage: pgvector inside the Postgres they already run, so close to zero extra. On Pinecone's Standard plan it would start at a $50 monthly minimum.
So the system costs a few hundred dollars a month to run. The engineer who makes it accurate costs far more, and a bad one costs the most: a support bot that gives wrong return-policy answers generates tickets instead of deflecting them. That is the math that should drive the hire.
The Honest Counterpoint
You may not need to hire RAG developers at all. If your corpus is small (a few hundred pages), long-context models can often take the whole thing in the prompt, and a plain search box plus a well-written FAQ may beat a chatbot. Several vendors also sell decent hosted assistants for help centers; for a commodity use case, buying one is faster than building.
And "RAG developer" as a separate job title is a bit of a fad. The skills are retrieval, data engineering and evaluation. A strong AI engineer or senior backend engineer with one shipped retrieval system covers it. Hiring a narrow specialist for a component that will stabilize in three months can leave you with an expensive person and nothing left to specialize in.
Frequently Asked Questions
Where can I hire RAG developers?
Look for senior AI or backend engineers who have shipped a retrieval system to real users, not people with "RAG" in the headline. Ruzora's AI engineer bench is screened with an AI interview and a graded coding assessment before anyone reaches a shortlist.
What does a RAG system cost to run per month?
For a mid-size support use case, usually hundreds of dollars, not thousands. The worked example above (30,000 questions a month) comes to about $390 on Claude Sonnet 5.5 or $195 on Haiku 4.5, plus storage. Prices change often; check the official pricing pages.
Do I need a vector database to build RAG?
No. If you already run Postgres, pgvector handles most startup-scale workloads. A managed vector database starts to earn its cost at larger scale or when you do not want to tune indexes yourself.
The Bottom Line
When you hire RAG developers, hire for measurement. The engineer who brings a labeled test set to the first week will beat the one who brings a favorite framework. Senior LATAM AI engineers work 5-7 hours inside US time zones, and we can send a vetted shortlist within 72 hours. Request a shortlist or browse AI engineers.
Roberto Espinoza is CEO of Ruzora, which helps US startups hire pre-vetted senior LATAM engineers, with a vetted shortlist in 72 hours. See available engineers.
