Hire LLM and RAG developers
Hire an LLM and RAG developer who tests on your data before launch
One engineer joins your team and builds the software around the model: answers grounded in your documents, agents that work in your tools, generative features in a product you already run. From $3K a month. No long-term contracts.
Free 30-min call. No pitch, no slides.
From $3K a month for one engineer.
See named client results in our case studies.
Free audit: your automation gaps and AI search visibility
Quick answer
AY Automate places LLM and RAG developers inside client teams. Typical timeline: engineers are live in 10 days from request. From $3K a month for one engineer.
How the work runs
From your documents to an answer with a source
The retrieval pipeline the engineer builds and tests before launch. The test set is the last step and the one that decides whether it ships.
Step 1
Your documents
Files, tickets, wiki pages and database rows. We agree what is in scope first.
Step 2
Chunks
Text split by meaning, with the source and date kept on every piece.
Step 3
Index
Stored in PostgreSQL or Supabase, or a vector store such as Pinecone or Weaviate.
Step 4
Answer with source
Claude or OpenAI answers from the retrieved text and points to where it came from.
Step 5
Test set
Real questions with correct answers. The pass rate is checked before launch.
Tools named here are the ones in our stack list further down this page.
Hire or place
LLM and RAG developer: placement against a US in-house hire
Figures are the ones published on our hire AI developers page. They compare one placed engineer with one US in-house engineer.
| Criteria | AY Automate placement | US in-house hire |
|---|---|---|
| Cost | From $3K a month (starting price, your quote may be higher) | $150,000+ a year |
| Employer taxes and benefits | $0 | ~$30,000 |
| Recruitment cost | $0 | $20,000-$40,000 |
| Time to start | Live in 10 days | 3-6 months |
| Fit risk | 90-day replacement guarantee | Yours to manage |
AY Automate placement
- Cost
- From $3K a month (starting price, your quote may be higher)
- Employer taxes and benefits
- $0
- Recruitment cost
- $0
- Time to start
- Live in 10 days
- Fit risk
- 90-day replacement guarantee
US in-house hire
- Cost
- $150,000+ a year
- Employer taxes and benefits
- ~$30,000
- Recruitment cost
- $20,000-$40,000
- Time to start
- 3-6 months
- Fit risk
- Yours to manage
Trusted by teams at
What it is
What an LLM and RAG developer does for your team
An LLM and RAG developer builds everything around a language model: which documents it may read, how they are split and indexed, what a good answer looks like, what each run costs. The model itself is the easy part to buy.
Your engineer starts by writing the use case down and collecting examples from your own documents, tickets or product, with the answers a good result should give. Every version is then tested against that set. You see how it performs before anyone relies on it.
The working stack is Claude and OpenAI models through your approved accounts, chosen per task after testing, in TypeScript and Python, with PostgreSQL and Supabase underneath and n8n for the glue. For vector search we configure stores like Pinecone, Weaviate or Supabase.
From $3K a month for one engineer. That is the starting price, so your quote may be higher. No long-term contracts. If the fit is wrong in the first 90 days, we replace the engineer at no extra cost. Engineers are live in 10 days from request.
Compare before you decide
Pick the engineer by the problem you have. The pages below cover the neighbouring roles and the guides we wrote for buyers.
Comparing options before you commit? how to hire a forward deployed engineer.
Fit check
Is this the right fit?
Where this wins
Teams sitting on documents nobody can search
Contracts, policies, tickets or manuals turned into answers with the source attached.
Products that need a generative feature
Drafting, summarising, chat over account data or classification, shipped inside the live product.
Teams with an agent that guesses
An agent that needs your private knowledge gets a retrieval layer first, then the broader automation.
When NOT to use this
- You want a foundation model trained from scratch. That is research lab work, and we do not do it.
- The job needs exact, repeatable output every time, such as a tax calculation. Ordinary code is cheaper and safer there.
- You have no examples to test against and nobody who can judge a good answer. Nobody can show the system works without them.
- You need one small task done. A monthly placement is built for ongoing work.
How it works
How the placement runs
Four steps, taken from our engineer placement process.
- 01
Requirement analysis
1-2 daysWe map your tech stack, the gap you are filling, and how the engineer fits your team.
DeliverableA written role brief
- 02
Team matching
1-2 daysWe match you with the engineer whose stack and experience fit your requirements.
DeliverableThe matched engineer
- 03
Deal closure
1 dayOnce terms are set, we place the matched engineer without a separate interview loop.
DeliverableAgreed terms
- 04
Placement and integration
Onboarding into your sprint, with a 90-day window to confirm fit.
DeliverableAn engineer working in your repo
Typical timeline
Engineers are live in 10 days from request
Stack we build with
Claude · OpenAI · TypeScript · Python · PostgreSQL · Supabase · Pinecone · Weaviate · n8n
Tell us the first thing it should answer
Share the documents, the stack and the first question your engineer should be able to answer. We come back with a match and a plan.
Use cases
What a placed LLM and RAG developer works on
Answers from your own documents
A retrieval layer so answers come from your verified knowledge base, with the source shown.
Agents with private knowledge
Agents that read and act in your tools, grounded in your data before they act.
LLM features in a live product
Generative features shipped inside a product that already has users, behind a test set.
Scope
What your LLM and RAG developer owns
In scope
- Use case definition and the measures it is judged by
- A test set built from your own examples
- Retrieval pipeline, agent or LLM feature on your data and systems
- Model choice per task, with cost per run estimated
- Logging, alerts and a monitoring plan
Not in scope
- Training foundation models from scratch
- Accuracy guarantees on data we have not tested
- Chatbots with no defined job or owner
- Day-to-day management of your engineer, which stays with you
Results
Related client work
Achieved results only. Clients that have not agreed to be named are described instead.
A platform built from the ground up in under a month that reads and summarises documents in Arabic, French and Darija.
The whole database became an agent anyone can talk to, with each person seeing and generating reports only for what they are allowed to touch.
Incoming delivery notes and invoices are read and matched automatically. Only the unclear cases reach a person, and the review team went from re-typing everything to spot-checking exceptions.
Pricing
What it costs
From $3K a month for one engineer. Starting price, your quote may be higher. We publish no team price, so a quote for several engineers comes after a call.
Starting price, one engineer
From $3K a month
Starting price, your quote may be higher. One engineer, full time in your sprint. No long-term contracts. If the fit is wrong in the first 90 days, we replace the engineer at no extra cost.
More than one engineer
Quoted after a call
The Golden Offer covers one to five engineers across roles. Scope and price are agreed on the kickoff call.
Next step
Describe the stack and the first job
Book a free call. From $3K a month, no long-term contracts, 90-day replacement guarantee.
Book a free callFree 30-min call. No pitch, no slides.
Free automation audit
Find what to automate first, and how AI sees you
We read your homepage, detect your tools, and check your Google rankings and AI Overview. You get a score out of 100. Add a work email to unlock automation opportunities matched to your stack.
Use Claude Code, Cursor or Codex? Paste this into your agent
Fetch https://www.ayautomate.com/SKILL.md and follow it: read my repo or website, pick the 2 to 3 manual loops worth automating first, and give me the booking link.
Bring your hiring goal, leave with a plan
- We look at the role you need and the systems it touches.
- You hear who we would place and what the first weeks look like.
- A straight answer on fit, even if it is not us.
How it works
- 01Requirement analysis. We map your tech stack, the gap you are filling, and how the engineer fits your team.
- 02Team matching. We match you with the engineer whose stack and experience fit your requirements.
- 03Deal closure. Once terms are set, we place the matched engineer without a separate interview loop.
- 04Placement and integration. Onboarding into your sprint, with a 90-day window to confirm fit.
“The team is super fast - sometimes we had to slow them down. We managed to scale the company without investing into hiring.”
Elie Salame
COO, Adstronaut.io
Teams we have built for
Where do we send your calendar link?
Recommended services
Hire forward deployed engineers
Engineers who learn your workflow first, then ship inside your team.
Hire Claude Code developers
Engineers who build with Claude Code every day.
Hire AI developers
Compare one placement with a small team.
Engineer placement
How we match, place and guarantee an engineer.
Golden Offer
One to five engineers across roles.
FAQ
Hiring an LLM and RAG developer
What does an LLM and RAG developer do?+
They build the software around a language model: retrieval over your documents, agents connected to your tools and LLM features inside your product. They also build the test set that shows whether each version works.
How much does it cost to hire an LLM and RAG developer?+
From $3K a month for one engineer. That is the starting price, so your quote may be higher once we know the access and systems involved. We publish no team price, so a quote for several engineers comes after a call.
How fast can I start?+
Engineers are live in 10 days from request. A comparable US in-house hire takes 3-6 months to start.
Is there a long-term contract?+
No. No long-term contracts. If the fit is wrong in the first 90 days, we replace the engineer at no extra cost.
When should I hire a forward deployed engineer instead?+
When the problem is still unclear and the engineer has to learn your workflow before they can build. Hire an LLM and RAG developer when you already know the stack and the first job. The forward deployed engineers page explains the other route.
Is this a project or a placement?+
A placement. One engineer joins your team and works from your backlog. If you want a scoped build with a written spec, see our generative AI development and RAG pipeline pages.
Still have a question? Ask it on a call.
Book a free 30-min call