Hire AI developers and engineers for software that already has users.
When you hire AI developers through Azendo, they join your product team full time and take features from demo to production: retrieval that stays accurate, evaluation that measures every change, and costs that stay under control. They work in your codebase, and a service delivery manager answers for what ships.
What should you look for when you hire AI developers?
When you hire AI developers for a live product, look for production experience over demo skills. Most of the work is retrieval quality, evaluation, monitoring, latency and cost per request, and model training is a smaller part. Developers who have run AI features with real users know where those features fail.
What your dedicated AI team covers.
Scope is agreed before your team starts. Most partners arrive with something already built: a demo, a proof of concept or a feature that works most of the time.
Your AI engineers take it the rest of the way. They build an evaluation set so every prompt change can be measured, add monitoring so quality drift shows up early, and design fallbacks so a slow provider slows the product down without breaking it.
- LLM features in existing products
Retrieval, structured output and tool use, built into software you already ship.
- Retrieval and context engineering
Chunking, embedding choice, reranking and the context around the prompt.
- Evaluation sets
A test set for model behaviour, so every prompt change is measured.
- Monitoring and cost control
Token spend, latency and quality tracked after launch.
- Model and vendor migration
Moving between providers without rebuilding the product around them.
Models, frameworks and retrieval tools.
Your AI engineers work with the providers you already use: OpenAI, Anthropic, Google, or open-weight models on your own infrastructure. They use frameworks like LangChain and LlamaIndex when they help and work directly with the SDKs when they don't. Retrieval runs on pgvector, Pinecone, Weaviate or Elasticsearch, with evaluation in Langfuse, Braintrust or a custom harness.
Model choice starts with your constraints. Data that has to stay in-house points to open-weight models on your own hardware, and latency and cost budgets narrow the rest. Everything is built so you can switch provider later without rewriting the product.
Choosing a provider in this field takes more care than in conventional software, so we wrote a checklist for choosing an AI development partner.
One fixed monthly fee for your AI engineers.
Companies hire AI engineers from us on the same model as every Azendo team: one fixed monthly fee for an agreed number of hours. See how the monthly fee is built.
Why companies hire AI engineers for the long term.
An AI feature in production keeps changing. Prompts behave differently when the underlying model updates, retrieval gets harder as your content grows, and costs creep up.
Your dedicated AI engineers handle that ongoing work: evaluation runs, regression checks on prompt changes, cost and latency monitoring, and migrations when a provider retires a model you built on. Most teams struggle to staff this part of AI engineering, and it is the part that keeps the feature working.
The AI engineering roles we assign.
Add one role or several. Each role has its own page showing what that specialist does in your team.
Hiring AI developers in-house, through an agency or with us.
Each option fits a different situation. An in-house hire suits permanent work close to home, and a local agency suits a project with a clear end date. A dedicated team from Azendo suits work that keeps coming, with recruiting, management and retention handled for you.
| Comparison | Building it in-house | Local agency | Azendo |
|---|---|---|---|
| Time to productive output | Months to recruit, onboard and ramp up | Fast to start, slow to learn your product | 4 to 6 weeks |
| Continuity of context | Resets when someone leaves | Rebuilt with each new project | Held by the same team, for years |
| Who answers for delivery | You do | Account manager, between projects | A service delivery manager, continuously |
| Cost profile | Fixed, whatever the workload | Priced per project | One monthly fee, adjustable each cycle |
| Scaling a discipline | A new hire each time | Re-scoped each engagement | Capacity up or down at the monthly cycle |
Questions about hiring AI developers.
What's the difference between AI outsourcing and an AI development partner?
Accountability after launch. A typical AI outsourcing project delivers a feature and ends. A development partner stays with the product, which matters because AI features need ongoing care in ways traditional software doesn't.
Do you build AI products from scratch?
Sometimes. We do our best work adding AI to software that already has users, revenue and real constraints.
Can remote AI engineers work with our existing team?
Yes, that's the usual setup. Your AI engineers work in your repositories and your sprints, alongside your own developers.
How do you handle data privacy?
We agree it before any work starts. Where data can't leave your infrastructure, the team works entirely inside it, with open-weight models if needed.
Are your specialists AI trained?
Yes. Every specialist completes AI certification through our Talent Success and Academy programme before joining a client team, whatever their discipline.
Often assigned alongside an AI engineer.
These disciplines often join the same team, under the same agreement.
AI engineering for the stage after the prototype works
A convincing demo is the easy part. What gets a feature into production is evaluation, retrieval quality, cost per request and handling the inputs you didn't expect.
The team follows the problem. Retrieval, evaluation and prompt behaviour sit with an LLM engineer. When the input is images or video, a computer vision engineer joins the team.
Your team works with the model providers you already have contracts with, inside your own accounts. We'll recommend the right model for each job, and sometimes the best answer is a simpler approach without one.
Most AI features are limited by the data behind them, so AI engineers often work alongside a managed data team on one committed monthly capacity.
Tell us what AI engineering capacity your roadmap needs.
Tell us about your roadmap and the team you have in mind. A service delivery manager will get back to you.
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