Hire AI Developers
Hire senior AI developers to design, build, and operate production AI — LLM features, retrieval over your own documents, document and image analysis, and predictive scoring. Engaged as an embedded team member or as a fixed-scope project with a written price.
Typical engagement: $45,000 – $180,000 · 8 – 20 weeks · US-based senior engineers
Three ways to hire AI developers here
Most companies do not need a permanent in-house AI team, and most do not need a pure staff-augmentation body shop either. Those are three different jobs, priced three different ways — pick the one that matches the problem in front of you.
Embedded in your team
A senior engineer (or a small pod) joins your standups, your repository, and your sprint board on a month-to-month basis and reports to your lead — useful when you have a roadmap and a product team but no one who has shipped an LLM feature before.
A fixed-scope AI project
Discovery first, then a written price and a milestone schedule for a defined outcome: a retrieval assistant over your documents, an extraction pipeline, a scoring model, or a copilot inside the app you already run. Best when the goal is clear and the budget needs to be.
Audit and roadmap first
A short paid assessment of the use case, the data you actually have, the model options, and the running cost — ending in a build plan, a realistic accuracy target, and a recommendation, including a recommendation not to build. Several ideas die here, which is the point.
Rescue an AI feature that stalled
A prototype that demos well and fails in production is the most common AI project we inherit: no evaluation set, no cost ceiling, no fallback when the model is wrong. We instrument it, measure it, and either harden it or replace the approach.
What AI development covers here
Applied AI in production, not research. The work is usually one or more of these, delivered inside a product that has to keep working when the model is wrong.
LLM features and copilots
Assistants, summarisation, drafting, classification, and search inside your own product — with prompt and model selection, structured output, streaming interfaces, fallbacks, and a cost per request you can predict before launch.
Retrieval over your documents
RAG pipelines that answer from your contracts, manuals, tickets, or case files instead of the open internet: ingestion, chunking, embeddings, hybrid search, citation of the source passage, and access control that respects who is allowed to see what.
Document, image, and audio analysis
Extracting structured data from PDFs, forms, photos, and recordings; classification and anomaly detection on operational data; computer vision where a camera already exists; speech transcription and diarisation.
Prediction, scoring, and forecasting
Estimating, pricing, churn, demand, and risk models trained on the data you already collect — with the feature pipeline, retraining job, and monitoring that keep the numbers honest after month one.
Also part of the job: evaluation (a labelled test set and a measured accuracy number, not a vibe), guardrails for regulated or customer-facing output, cost and latency control, and integration with the CRM, ERP, EHR, or data warehouse you already run. Custom app development covers the surrounding application when the AI feature needs a product built around it.
How an AI project runs here
The same five steps as any other build, with two additions specific to AI: a feasibility check before anyone commits to a price, and a measured accuracy target that the build is judged against.
Scoping call and data check
A short call to understand the workflow, the users, the systems involved, and what success looks like. No charge, no obligation.
Feasibility and a fixed price
Does the data support the outcome, which model class fits, and what will it cost per request? Then a written scope, price, and milestone schedule.
Evaluation set before features
A labelled test set and a target metric are agreed early, so “it looks good” is replaced by a number that can regress and be caught.
Build in reviewable milestones
Senior engineers build against the agreed scope and ship working increments with the evaluation results attached, so feedback arrives while it is cheap.
Launch, monitor, hand over
Deployment, cost and quality dashboards, and a 6-month window in which any bug inside the delivered scope is fixed at no extra cost — with the code in your own repository.
When hiring us is the wrong call
We would rather say this before the call than after the invoice arrives.
- You need a foundation model trained from scratch. That is a research-lab budget, not a project budget — we build on existing models, tuned with your data.
- The data does not exist yet. If the outcome depends on records nobody has been collecting, the honest first project is a data collection plan, not a model.
- You want the lowest hourly rate. We are not an offshore rate play; the price buys senior engineers who have put AI features into production and know where they break.
- You need 24/7 staffed operations. Round-the-clock human coverage is a different service than engineering build and support.
The stack AI work is built on
Chosen so the second engineer to touch the codebase — yours — is not fighting it, and so the model can be swapped when the market moves.
- Models: commercial LLM APIs where they fit, open-weight models self-hosted where data residency, volume, or cost demands it
- Retrieval: vector search, hybrid and keyword search, embedding pipelines, document parsers
- Application: Python, TypeScript, Node.js, Next.js, React — the same stack the rest of the product runs on
- Data: PostgreSQL, Redis, object storage, warehouse and BI integrations
- Operations: AWS, Docker, CI/CD, evaluation harnesses, cost and quality monitoring, backups
Already have a model, a vector store, or an ML engineer? Good — we work inside what you have. See the full list of services, our guide to what an AI consultancy actually does, or products we have shipped.
Hiring AI developers: frequently asked questions
How much does it cost to hire AI developers?
Most engagements run $45,000-$180,000 over 8-20 weeks. An AI feature added to an application you are already building costs less, because it rides on work that is already paid for. An embedded engineer on your team is billed monthly, and the short paid audit is a fixed low-four-figure number quoted before it starts.
What AI work can you take on?
LLM features and copilots inside your product, retrieval over your own documents with citations and access control, document, image and audio analysis, and predictive or scoring models trained on data you already collect. The common thread is production systems with an evaluation set, cost controls and a fallback when the model is wrong.
Do we need our own data or can you start from scratch?
Some data is required, and it does not have to be clean. For retrieval work the documents you already have are the starting point. For prediction and scoring models, the historical records matter: if the outcome depends on data nobody has been collecting, the honest first project is a measurement and collection plan rather than a model.
How is an AI project different from normal software development?
Three things: an evaluation set agreed before features are built, a cost and latency budget per request, and a defined fallback for the cases the model gets wrong. Without those, an AI feature demos well and fails quietly in production, which is how most inherited AI projects arrive here.
Can you work with our existing ML team or model?
Yes. We regularly join a team that has data scientists but no one who has shipped a production feature, and we work with a model, vector store or pipeline you already run rather than replacing it. The code is delivered in your own repository with documentation, so your team is never locked out.
How quickly can an AI developer start?
A scoping call can usually be booked within a few days, and work starts after the scope and price are agreed. That is typically one to two weeks from first contact for a fixed-scope project, and sooner for a single embedded engineer.
What happens after launch?
Any bug inside the delivered scope is fixed at no additional cost for six months after launch. After that, monitoring, evaluation reruns, model and prompt updates, and ongoing development are available on a support plan.
Scope the AI feature before you price it
Send the workflow you want the model to take over, and we will come back with what the data supports, a fixed-scope price, and a schedule — not an hourly estimate that moves.