Hiring AI engineers: what to look for in 2026

Almost every company now wants to add AI to its product or operations: smarter search, assistants for customers, automated document processing, forecasting, recommendations. But "AI engineer" can mean very different things, and hiring the wrong profile is easy.

This guide explains the main roles, what to test and how to scope your first project so it delivers real value.

Understand the different roles

• AI application engineers build product features on top of existing models, including large language models: retrieval over your documents, assistants, summarization, extraction and agents that use tools. They combine strong software engineering with practical knowledge of model behavior.
• Machine learning engineers train, fine-tune, deploy and monitor models, and build the infrastructure around them.
• Data scientists explore data, build statistical and predictive models and run experiments.
• Data engineers build the pipelines that make data available and reliable, which every AI project depends on.

For most companies starting with AI features, an AI application engineer with solid software skills is the right first hire. See our AI/ML engineers.

Start with the problem, not the technology

Good AI projects start with a specific, valuable problem:

• "Support agents spend hours searching documentation to answer tickets."
• "We process thousands of invoices by hand every month."
• "Customers cannot find the right product in our catalog."

Then ask: what would a good result look like, and how will we measure it? A clear problem and metric make it much easier to hire the right person and judge their work.

Skills to look for

• Strong software engineering: clean code, testing, APIs and deployment. AI features are software features.
• Practical model experience: choosing models, prompting, retrieval, tool use, handling cost and latency.
• Evaluation: building test sets and measuring quality systematically, rather than judging by a few examples.
• Data handling: preparing, cleaning and protecting the data the system uses.
• Product sense: designing experiences that handle uncertainty and mistakes gracefully.
• Security and privacy awareness: protecting sensitive data and guarding against misuse.

Questions to ask

• Walk me through an AI feature you shipped to real users. How did you measure whether it worked?
• How do you evaluate the quality of a model's outputs over time?
• How do you decide between using an existing model, fine-tuning one or building something simpler?
• How do you control cost and latency in production?
• What can go wrong with AI features, and how do you design around it?
• How do you handle sensitive data in AI systems?

Strong candidates talk about evaluation, trade-offs and failure modes, not just impressive demos.

A practical test

Give a small, realistic task: for example, build a prototype that answers questions over a few of your public documents, with a simple evaluation of answer quality. Look at how they:

• Structure the system
• Measure quality
• Explain limitations
• Handle edge cases and errors

Pay for anything longer than a few hours. See paid trial projects.

Scope your first project small

A good first AI project:

• Solves a specific, measurable problem
• Can show results within weeks, not months
• Has a human in the loop where mistakes are costly
• Uses data you already have and are allowed to use

Start with an internal tool or a limited feature, learn, then expand.

Data readiness

AI is only as good as the data behind it. Before hiring, check:

• Is the data you need accessible and reasonably clean?
• Do you have the rights to use it for this purpose?
• Is sensitive data identified and protected?

If not, a data engineer may be the right first hire.

Common mistakes

• Hiring researchers for product engineering work
• Judging quality by a few impressive demos
• Ignoring cost and latency until production
• No plan for monitoring quality after launch
• Starting with the hardest possible problem

Hire vetted AI engineers

Request a shortlist of vetted AI/ML engineers and get candidates within 48 hours.