Hire AI developers who take AI apps to production
Hire Caudify's AI developers when you need AI features or a whole AI app built and running in production. They are full-stack engineers who integrate LLMs, build retrieval and agent workflows, set up evals and monitoring, and ship the web or mobile product around the model, embedded in your team or as a dedicated pod.
- Remote, worldwide
- Embedded, pod or project
- You own the code
- Human review on every change
What Caudify's AI developers build for clients.
AI features in an existing product
Search, summaries, drafting, classification and assistants added to the app your customers already use.
New AI apps
Web and mobile apps where a model does core work, from prototype on your data to a monitored release.
Internal AI tools
Assistants and automations over your documents, tickets and systems, with per-user permissions.
The plumbing
Evals, tracing, cost controls and a provider-agnostic model layer, so quality holds after launch.
Skills and tools AI developers bring.
AI engineering
- LLM APIs (Claude, OpenAI, open-weight)
- Prompt and structured-output design
- RAG and vector search
- Tool calling and agents
- Evals and tracing
Product engineering
- TypeScript, Next.js, React
- Python and Node.js APIs
- PostgreSQL and pgvector
- React Native for mobile
- CI/CD and cloud deployment
Ways of working
- Small, reviewed pull requests
- AI pair programming with human review
- Static analysis with Caudilysis
- Written decisions and runbooks
How to judge AI developers, wherever you hire them.
Useful whether you hire from Caudify, a freelancer platform or your own pipeline. The full checklist is in how to hire AI developers.
What good looks like
- They talk about evaluation before they talk about models: how they will know the feature works.
- They have shipped AI to real users and can describe a failure they caught after launch.
- They can build the non-AI parts too: auth, data model, API, interface. Most of an AI app is ordinary software.
- They are specific about cost and latency, not just quality.
Questions worth asking
How to hire AI developers from Caudify.
The same three models apply to every role. Compare them with freelancers and in-house hiring.
Embedded engineers
Caudify engineers join your standups, repositories and tools, and work from your backlog under your technical lead.
- Best when
- You have a product team and need AI depth inside it.
- You bring
- Direction and priorities
- Caudify brings
- Senior AI engineering, code review, Caudilysis scans on our changes
Dedicated AI pod
A small cross-functional Caudify team owns an AI product area end to end: discovery, build, evals, release and iteration.
- Best when
- You need a whole AI capability built and run, not extra hands.
- You bring
- Goals and decisions
- Caudify brings
- Delivery, quality, evals, hosting and reporting
Scoped project
A defined scope, milestones and acceptance criteria agreed up front, delivered and handed over with documentation.
- Best when
- The outcome is clear: a RAG assistant, an agent, an integration.
- You bring
- Acceptance and feedback
- Caudify brings
- The finished, tested, documented system
Prefer a defined outcome?
The same engineers deliver these as scoped services.
Need a different mix?
Reading for teams hiring AI developers.
- Engineering · 7 min readHow to hire AI developers: skills, interview questions and red flagsMost AI products are mostly ordinary software with a model in the middle. Hire for evaluation discipline and production experience first, model trivia last. Here is the checklist we use.
- AI · 5 min readAI-assisted software development: how we use LLMs from discovery to releaseAI makes the mechanical parts of software delivery faster. It doesn't make decisions, and it doesn't skip review. Here is where LLMs sit in each stage of our delivery, and the rules that keep them honest.
Hiring AI developers, answered.
What does an AI developer do?
An AI developer builds software where a machine learning or language model does part of the work. Today that usually means integrating LLMs into products, building retrieval over company data, wiring tools and agents, and setting up the evaluation and monitoring that keep AI features reliable.
AI developer or machine learning engineer: which do we need?
If you are building on existing models (LLM APIs or open-weight models) into a product, you need AI developers. If you are training models from your own data for prediction or vision tasks, you need machine learning engineers. Many AI products need mostly the first, with occasional help from the second.
Can your AI developers work in our codebase and process?
Yes. Embedded engineers work in your repositories, tickets and rituals, follow your conventions, and open pull requests your team can review.
How much does it cost to hire an AI developer from Caudify?
It depends on the engagement model, the skills involved and how long you need them, so we quote per scope after a free scoping conversation. We don't publish a rate card, because a RAG prototype, an embedded senior engineer and a dedicated pod are priced very differently.
Tell us what you're building.
A free assessment is the start of every engagement. We look at the product, the data and the constraints, then come back with an honest view of whether hiring AI developers is the right move and how we would staff it.
Looking for a job instead? See open roles at Caudify.