Hire an AI engineer, an AI consultant or an AI Product Lead?

Hire for the missing responsibility. An AI engineer is a sensible starting point when the problem is defined and the technical system needs building. A consultant can help investigate or deliver a bounded piece of work. An AI Product Lead is useful when someone must connect customer needs, priorities, delivery and adoption over time. Actual titles overlap, so test the remit rather than treating this as a universal job taxonomy.

There is a particularly expensive vacancy that asks one person to discover the business problem, pick a model, connect every system, design the interface, win employee trust and accept responsibility for a revenue target. Sometimes a broad builder can cover several parts. The interview still needs to reveal which parts the company will support.

Describe what will be different in three months

Write a concrete outcome before writing a list of tools. A team might need a working document workflow, a justified decision about an AI opportunity, or a product direction that several departments can actually follow. Those are different starting conditions. “Be our AI champion” does not tell a candidate which decision they can make on Monday.

Then name the decision owner, existing team, systems involved and constraints. Someone can be excellent at implementation and still fail in a role that secretly requires resolving a disagreement between founders. That is not a lack of prompt-engineering enthusiasm.

A role comparison for a real hiring discussion

These are my hiring distinctions, not official definitions. NIST’s description of AI actor tasks is a useful reminder that development, deployment, domain input and evaluation involve different responsibilities. A small company may combine them. Combining them should be an explicit operating choice.

A 45-minute conversation with something real to discuss

Here is an original interview structure you can adapt. It is not a validated assessment instrument. Use a fictional or anonymised scenario and assess only work that is relevant to the role. A substantial take-home task should have a clear time limit and appropriate compensation; do not turn candidates into an unpaid delivery team.

1. Ten minutes: explain the current process, the desired result and what is unknown.
2. Fifteen minutes: ask the candidate to identify the missing information and propose a first step.
3. Ten minutes: introduce a complication, such as incomplete data or a stakeholder who disagrees.
4. Ten minutes: discuss quality, ownership, handover and what would justify stopping.

The U.S. Office of Personnel Management describes work samples as tasks resembling actual job activities. It also notes that they should assess competencies expected on entry. That matters when you are willing to train someone: do not quietly test a skill you never said they needed to arrive with.

Listen for the missing questions

An engineer might ask about access, system behaviour, test cases and failure modes. A product lead should also ask whose problem matters, which outcome changes the decision and what will be deprioritised. A trainer needs to understand the participants’ existing work and what they can safely practise. A consultant should make assumptions and the limits of an engagement visible.

Strong candidates need not use your preferred vocabulary immediately. They do need to connect their choices to the task. Someone who can explain why an ordinary integration is sufficient may be more useful than someone who proposes agents before hearing the workflow.

Make the evaluation consistent

Before the interviews, agree on evidence categories such as problem clarification, practical delivery, evaluation, communication and ownership. Record observations separately from confidence or personal chemistry. An impressive company name is a reason to ask about the person’s contribution, not an answer to that question.

Ask for the constraints behind a previous result and what the candidate personally decided or built. Accept anonymised explanations when confidentiality matters. A person who can respect a former employer’s information boundary may also respect yours. The absence of a public case study is not proof of an absence of work.

When the project is too small for a full-time role

A bounded engagement can clarify a workflow or test a first integration while the company learns what it needs. A continuing product mandate can justify a longer relationship. Do not call an engagement “fractional leadership” if the person has no decision rights, no access to users and no route to the actual sponsor.

I combine product leadership, hands-on AI delivery and corporate training, with the detailed responsibilities shown in my professional profile. For a defined implementation challenge, the AI integration page explains a useful starting point. If your needs span several responsibilities, bring that ambiguity to the conversation. It is more productive than hiding it under a fashionable title.

Questions people ask

Should I hire an AI engineer or an AI Product Lead?

Start with the responsibility you are missing. A defined system that needs implementation points towards engineering. Unclear customer problems, competing priorities and ownership across delivery and adoption may require product leadership. Some people combine these capabilities, but the remit should be explicit.

What should I ask an AI integration consultant before hiring?

Ask what they will deliver, what you must provide, how quality will be evaluated, who owns the result and what happens after handover. Request an explanation of assumptions and comparable responsibilities rather than only a list of tools.

Can Daniil Shakhovskiy work with teams outside Czechia?

Daniil is based in Olomouc, Czechia, and is open to teams worldwide. He works in English, Czech and Russian. Role or project fit, timing, scope and location requirements should be discussed directly.