Hire AI engineers
Senior AI engineers and AI developers who join your team to add retrieval, LLM features and agents to a product that already exists — and the evaluation work that separates a demo from something you can charge for.
From $4,000 per engineer per month · start within 1–2 weeks · three-month minimum · the IP is yours from day one · how this is priced · or have us deliver it fixed-price
What hiring an AI engineer here gets you
The terms, before the pitch.
Senior and mid+ AI engineers on your board, billed per engineer per month with a three-month minimum — AI staff augmentation rather than an agency handoff. Evidenced by a multilingual document platform with a custom retrieval architecture and ERP integration that cut manual processing by 87%.
There are two ways to buy it. Take the engineers when you have a product owner and a backlog and the gap is AI capacity. Take the outcome when you would rather hand over a defined result: the same engineers deliver it as a fixed-price project — see AI development services.
The role is mostly not model work. On a production AI product the largest block of engineering is data — ingestion, cleaning, chunking, permissioning — followed by integrations, and only then the pipeline itself. An engineer who wants to spend the first month choosing a model and the last month discovering the corpus is unusable has optimised the wrong variable.
How this is priced · What a team costs, by engineers and months · How it compares with an in-house hire · Staff augmentation: every role we staff
What these engineers have measured in production
One platform for a multilingual logistics operation, client anonymised. Every figure below comes from it, and every one of them is on the case study.
87% less manual processing
At 99.2% extraction accuracy — a measurement taken by an evaluation harness built for the purpose, not an estimate.
About $0.011 per answer
Over roughly 40,000 queries a month. Retrieval is the cheap fifth of it; what retrieval really costs arrives as extra input tokens in the generation line.
1.9 s average, 4.2 s p95
Measured to the last token rather than the first, because that is the wait a user actually sits through.
14 sources in 5 languages
Reconciled into one retrieval layer over about 2,500 documents, reindexed daily, and wired into the ERP that already existed.
10 weeks to production
One team across the pipeline, the product and the integration, rather than three handing work between them.
What an AI engineer covers here
Six things this role owns, rather than a list of technologies.
Retrieval pipelines
Ingestion, chunking, embedding and ranking. Chunking strategy is the highest-leverage knob in the whole system and the least discussed one.
Permission-aware retrieval
Enforcing at query time who is allowed to see which document — the requirement that rules out fine-tuning for most enterprise cases, since training data is available to everyone who reaches the model.
LLM integration
Provider abstraction, streaming, fallback and cost control, so swapping a model is days of work rather than a rewrite.
Evaluation harnesses
Scored test sets and regression detection, so a quality drop is a failed build instead of a support ticket three weeks later.
Agentic systems
Tool use, orchestration and human-in-the-loop checkpoints, built with the failure modes designed in rather than discovered.
Cost and latency work
Token budgets, caching and routing between models. A feature that is cheap at a hundred users can be the largest line item at ten thousand.
The stack we staff for
What these engineers work in day to day.
Models
Frameworks
Retrieval
Engineering
How the engagement works
Six steps from a role brief to an engineer on your board.
Define the role
The stack, the seniority, and what the first month has to produce. That last part is the one most role briefs leave out, and it is the one that makes a shortlist useful.
Meet the engineers
You interview, against your bar and your process. If we do not have the right person for the role we say so rather than putting forward the nearest one — a bad match costs you a quarter.
Contract and NDA
An NDA before any of your code or design material is shared. You hold 100% of the IP from day one. Work can start within 1–2 weeks of signing.
Onboard into your process
Your board, your repo, your standups, your review conventions. The target for week one is a small change landed in your pipeline, not an architecture proposal.
Run it monthly
Billed per engineer per month, $4,000–$7,000 by seniority and from $4,000. Three months minimum, then rolling — below three months onboarding eats most of the value and both sides know it.
Scale or stop
Add engineers as the work grows, or end it with notice. A team of two to eight plus a tech lead is the usual shape, with a QA engineer when the work needs one.
When this is the wrong choice
Outstaffing is the wrong shape for some of the work people bring to it, and finding that out in month two is expensive for everyone.
You need research, not engineering
Training a model from scratch or pushing a state-of-the-art benchmark is a research role. These engineers build products on models that exist.
Nobody can say what "good" looks like
Without a definition of a correct answer there is nothing to evaluate against, and quality becomes an opinion. Settling that is a week of work and it comes first.
The corpus does not contain the answer
No pipeline fixes missing information. Worth taking a genuine random sample of your documents — not the tidy examples — before scoping anything.
Common questions
The six that come up on nearly every first call about this role.
What this role has shipped
A delivery that evidences the work above.
Need an AI engineer on the board?
Tell us the role, the stack and when you need someone starting. We come back with who we can put on it and what it costs, in writing.
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