Hire RAG and LLM developers for answers grounded in your own data

Retrieval augmented generation lives or dies on retrieval. Cubehire RAG developers get the unglamorous parts right: chunking, metadata, hybrid search, reranking and evaluation, so the model answers from the right paragraph and shows where it found it.

Hire RAG and LLM Developers How we vet them
The work

What our RAG and LLM Developers build

Typical projects companies bring a RAG developer in for.

1

Internal knowledge assistants

Search and chat over wikis, policies, tickets and shared drives with permissions respected per user.

2

Customer facing help search

Answers from your help center and product docs with citations, so support deflection does not mean wrong answers.

3

Document ingestion pipelines

PDF, HTML and spreadsheet parsing, chunking and embedding jobs that keep the index fresh as documents change.

4

Retrieval evaluation

Test questions with expected sources so every change to chunking or prompts is measured, not guessed.

Skills

Skills we test every RAG developer on

  • Embeddings
  • pgvector, Pinecone or Weaviate
  • Hybrid and keyword search
  • Reranking
  • Chunking strategies
  • LlamaIndex or LangChain
  • Python
  • Document parsing
  • Metadata filtering
  • RAG evaluation
  • Access control
  • FastAPI
Vetting

How we vet RAG and LLM Developers

Every RAG developer sits the Cubehire AI interview: multiple choice fundamentals, a timed written and coding round, and a recorded video round. A person on our team then reviews the profile before any company sees it. For this role, the interview checks that they can:

  1. Check 1

    Chooses a chunking and retrieval approach for a messy document set and defends the trade offs.

  2. Check 2

    Diagnoses why a RAG system returns confident wrong answers and lists what to measure.

  3. Check 3

    Explains how user permissions carry through to what the model is allowed to read.

Available now

Vetted RAG and LLM Developers in the network

Each profile carries a scored AI interview and a human review.

See the whole network

Tell us about your RAG developer role

New profiles are approved every week. Describe the role and we will send matched, interview scored RAG and LLM Developers within 48 hours.

Describe the role
How it works

Hire in 4 easy steps

From the first conversation to a signed engineer, without the six-week search in between.

01
Define
Tell us what you need

Describe the role, the stack and the work. No job spec required, a conversation is enough.

02
Discover
Meet the top engineers

Three to five matched engineers within 48 hours, each with their AI interview score attached.

03
Evaluate
Interview with ease

Pick who you want to meet and we handle the scheduling on both sides.

04
Onboard
Hire with confidence

Contracts, USD payroll and compliance handled. You get an engineer, not an admin project.

Terms

Simple terms for US companies

Shortlist in 48 hours

Three to five interview scored profiles that match your stack, delivered within two days.

Hours that overlap yours

A shifted working day agreed during hiring, with daily overlap with Eastern or Pacific time.

Contracts and payroll handled

We carry the paperwork and pay the developer in USD, so you are not opening an entity abroad.

Replacement guarantee

If the fit is not right, we replace the developer at no extra cost within the guarantee period.

Hiring RAG and LLM Developers

Questions about hiring RAG and LLM Developers

What companies ask before they hire a RAG developer through Cubehire.

Talk to our team
RAG, or retrieval augmented generation, finds relevant passages in your data and gives them to a language model to answer from. The model is the easy part; retrieval quality, freshness and permissions are where most projects fail, and that is what a RAG developer focuses on.
If you already run Postgres, pgvector is often enough. Pinecone, Weaviate or Qdrant make sense at larger scale or when you need managed hybrid search. Your developer will recommend one based on your data volume and stack.
Yes. Access rules are stored as metadata on every chunk and applied as filters at query time, so a user only gets answers built from documents they could open themselves.
With a set of real questions and the passages that should answer them. Retrieval hit rate and answer faithfulness are tracked on every change before it goes live.
A flat rate quoted before you interview anyone: a one time fee for full time placements, or a fixed monthly rate for contract work. There is no percentage of salary. See the pricing page for current rates.

Still have a question about Cubehire?

Ask about vetting, pricing, contracts or how the AI interview works. Cubie, our assistant, answers straight away and raises a ticket for the team when it cannot.

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Ready to hire a RAG developer?

Tell us the stack and the work. You get matched, interview scored RAG and LLM Developers within 48 hours.

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