Hiring guide
How to hire an AI developer
Last reviewed , published by Gjethe
The short answer
To hire an AI developer, start with a specific business problem rather than a technology; look for shipped projects using language models, retrieval or automation; ask how they evaluate accuracy, prevent wrong answers, protect data and control usage costs; and make sure the solution runs in accounts you own with clear documentation.
AI development has attracted many newcomers, and impressive demos are easy to build. Systems that work reliably with real users and real data are much harder. The difference shows in how developers talk about evaluation, edge cases, privacy and cost. This guide gives you the questions that reveal real experience.
Common AI projects
| Project | What it does |
|---|---|
| Chatbot or assistant | Answers questions from your content and hands over to staff |
| RAG system | Searches your documents and answers with citations |
| AI agent | Takes actions in other systems based on instructions |
| Automation | Processes emails, documents or data with AI steps |
| AI features in products | Summaries, classification, generation inside your app |
Signs of real AI experience
- Shipped projects with real users, not just demos
- A clear method for testing answer quality
- Strategies to reduce hallucinations
- Understanding of data privacy and provider terms
- Monitoring and cost control in production
- Willingness to say when AI isn't the right tool
Questions to ask
| Question | Why |
|---|---|
| How will you measure accuracy? | Without evaluation, quality is guesswork |
| What happens when the AI doesn't know? | Safe fallbacks prevent wrong answers |
| Where does our data go? | Privacy and contractual obligations |
| How will usage costs be controlled? | AI fees scale with use |
| Which provider and why? | Model choice affects cost, quality and data terms |
| Who owns prompts, code and accounts? | Avoids lock-in |
Start with the problem
The most successful AI projects start with a narrow, valuable problem: answering the top support questions, extracting data from invoices, summarising long documents for staff. Broad goals like “add AI to our business” lead to unfocused projects.
Describe the task, the people involved, the data available and what a good result looks like. A good developer will help refine this and suggest a small pilot before a larger build.
Red flags
Be careful with developers who promise perfect accuracy, can't explain how their system handles errors, send sensitive data to services without discussing terms, or build everything on their own accounts.
Also be wary of solutions that depend entirely on one prompt with no testing, monitoring or human review where stakes are high.
Plan for maintenance
AI systems need ongoing care: updating knowledge, reviewing conversations, adjusting instructions, adapting when providers release new models or retire old ones, and watching costs.
Agree upfront how monitoring and improvements will work after launch.
Ask how they test AI systems
Testing AI is different from testing normal software, because answers vary. Experienced developers build test sets of real questions with expected answers, measure accuracy and track changes when prompts, data or models change.
If a candidate can't describe how they'd know whether the system is working well, keep looking.
AI project risks and how to manage them
| Risk | How good teams manage it |
|---|---|
| Wrong answers | Grounding in your content, testing, human review |
| Data leaks | Minimal data sharing, access controls, provider terms |
| Rising costs | Model choice, caching, usage limits and monitoring |
| Provider changes | Abstraction layers and documented prompts |
| User misunderstanding | Clear disclosure that users are talking to AI |
Prepare your data before you hire
Collect the documents, FAQs, policies or data the system will use and identify who owns each source. Outdated or contradictory information leads to poor AI answers.
This preparation often improves your internal documentation as a side benefit.
Pilot first, then scale
A short pilot on a focused use case, for example answering a subset of support questions, proves whether AI adds value before larger investment. Define success criteria upfront, such as accuracy on a test set or time saved.
If the pilot works, scale it; if not, you've learned cheaply.
What a good AI proposal includes
- The specific problem and success measures
- Data sources and how they'll be prepared
- Model and provider choices with reasons
- Evaluation and monitoring approach
- Privacy and security measures
- Estimated usage costs
Freelancer, agency or in-house
Freelance AI developers suit focused prototypes. Agencies suit projects needing design, integration, security and maintenance alongside AI. In-house teams make sense when AI is central to your product.
Whichever you choose, ensure knowledge is documented so the system can be maintained.
Explaining AI to your users
Users should know when they're interacting with AI and how to reach a person. Clear labels, limits on sensitive topics and easy handover build trust.
A good developer designs these elements into the product rather than adding them later.
Frequently asked questions
Do I need a data scientist?
For most business AI applications built on existing language models, an experienced AI developer is enough. Custom model training is rarer and needs specialised skills.
Which AI provider should I use?
It depends on quality, cost, data terms and where data is processed. A good developer can compare options for your use case.
Can AI systems be GDPR-compliant?
They can be designed to support compliance through data minimisation, appropriate provider terms and clear information to users. Your legal adviser should confirm your approach.
How do I avoid lock-in?
Keep accounts in your name, document prompts and architecture, and use designs that can switch providers.
Should we fine-tune a model?
Often not. Retrieval from your own content usually works better for knowledge questions. See RAG vs fine-tuning.
How much does it cost to run AI?
Usage fees depend on volume and model. Ask for an estimate based on expected usage.
Can we start with a pilot?
Yes, and it's usually wise: a focused pilot proves value before larger investment.
Who reviews AI output?
For high-stakes uses, design human review into the process.
Can AI developers work with our existing systems?
Yes, through APIs and integrations, so AI features can use your data and trigger actions.
How do we handle AI regulation?
Requirements are evolving. Choose a developer who follows transparency and data-protection good practice, and involve your legal adviser for regulated uses.
Can AI developers also build the user interface?
Many can, or work alongside designers and web developers to integrate AI features into your product.
How do we keep AI costs predictable?
Set usage limits and alerts, choose efficient models and monitor costs from the first day.
Can we switch AI providers later?
Yes, if the system is designed with that in mind. Ask developers how hard switching would be.
Do we need our own GPU servers?
Usually not. Most business AI applications use hosted models through APIs.
Can AI tools run in the EU?
Several providers offer EU data processing options. Check each provider's current terms.
Related services
AI Chatbot Development
Chatbots that answer from your own content.
RAG Systems
AI that searches your documents and cites them.
AI Agent Development
AI that takes actions in your tools, with guardrails.
AI Automation
Make, Zapier, n8n and Python workflows with AI steps.
AI API Integration
Language, vision and speech models in your software.
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