Should I hire an AI engineer? What they do, when it pays, and the alternatives

Most of what you find when you search "how to hire your first AI engineer" is recruiting advice for US start-ups: where to post, what to ask in the interview, what to pay. This piece starts one step earlier, from the employer's side: what an AI engineer actually does inside a company, when a hire is the right move, and what the other options are before you open a role.

What does an AI engineer actually do in a company?

In a typical company an AI engineer spends little time on models and most of it on everything around them: data, integration, evaluation and people. The model is usually bought from a large provider through an API. Their job is to make it useful inside your processes.

In the first months, a good AI engineer:

• finds the data the use case needs and works out who can grant access to it, in the ERP, the accounting system, shared mailboxes and file servers;
• integrates the system with what already exists: invoices, the CRM, the ticketing tool, the internal application with no API;
• builds an evaluation set (real examples with the correct answer), so they can say with numbers whether the system works rather than from impression;
• puts it into production with access rights, logging and cost monitoring per request;
• works with the people who will use it, because a system the team routes around produces nothing.

This is why "AI engineer" covers very different profiles. A researcher who trains models from scratch and an engineer who takes an AI agent into production over your data are not doing the same job. For a company that wants to automate its operations, the second profile is almost always the one that matters.

When does a full-time AI hire make sense?

A hire makes sense when AI becomes a permanent capability of the company rather than a project. The signs are fairly clear:

• You can name at least three use cases for the next 18–24 months, not one.
• The data exists and can be reached. Not perfect, but you know where it lives and who owns it.
• Someone can manage them: a CTO, a tech lead or a product owner who understands enough to set direction and judge the work.
• AI sits close to your product, meaning what you sell or what sets you apart, and the know-how has to stay in-house.
• You can fund the roles around them, at least part-time: data, infrastructure and security.

If all five hold, hire. Probably not one person, but the first of a small team. We cover the in-house-versus-outsource choice in more depth in how to decide whether to build AI in-house or outsource.

When is hiring an AI engineer the wrong move?

In three situations, and they are the most common among companies starting now.

You have one use case. An engineer hired for one project finishes it, and then either leaves or drifts into other work. You pay for a permanent role to meet a temporary need.

The data is not ready. A very good engineer's first months will go on cleaning exports and chasing access. The work is necessary, but it is not what you hired them for. Read how to get your company data ready for AI before you open the role.

Nobody can manage them. The most expensive scenario is a lone AI engineer in a company with no technical team. Nobody checks their decisions, nobody can take over, and if they leave, the system leaves with them. Before the hire, someone has to own AI in the company. If that question is still open, Vlad Tudor has written about how to structure your company for AI and who should own it.

All three matter for the same reason: expertise is the scarcest resource. Among EU enterprises that considered AI in 2025 and did not proceed, 70.3% named a lack of relevant expertise, nearly double the 38.4% who named cost (Eurostat, KS-01-26-009, Table 7, published 26 March 2026). A scarce person should be placed where there is something to build.

Should I hire an AI engineer or an agency, and what are the other options?

There are five real options. They differ less in who writes the code than in who answers for the result and what stays with you afterwards.

In short: a hire and staff augmentation give you capacity, but the result stays your problem. An agency gives you a result, but usually works from outside the company, to a specification written before anyone has seen the data. The embedded engineer is the option in which someone from outside answers for the result from inside your team.

The EU's own data shows the pattern. Among enterprises already using AI in 2024, 45.8% of large ones used systems developed or modified by external providers, against 29.7% of medium-sized ones (Eurostat, KS-01-26-009, reference year 2024). Large companies buy expertise in. Mid-sized ones more often try alone.

What drives the real cost of an AI engineer?

Salary is the visible part and rarely the large one. The real cost of an AI engineer comes from a handful of drivers worth writing down before you compare offers.

1. Seniority. A junior needs someone to guide them. A senior who has shipped systems to production costs more per month and less per result.
2. Time to start. Searching for rare profiles takes time. In Germany it takes 7.7 months on average to fill an IT position, unchanged since 2023 even though open roles fell (Bitkom Research, January 2026). Across the EU, 57% of firms with open IT vacancies reported difficulty filling them in 2024, particularly senior and specialised roles including AI (Eurofound, July 2026). The project waits for all of it.
3. Ramp-up. Even a strong hire needs time to learn your processes, data and people before anything reaches production.
4. Retention. Good AI engineers are actively recruited. If they leave, you lose the person and the knowledge of the system, and the search starts again.
5. What they need around them. Data access, cloud or on-premise infrastructure, model costs per request, a security review, someone able to judge their work. One AI engineer alone is rarely enough.
6. Management time. Someone in the company has to set direction, take decisions and unblock access. That time is a cost even when it never appears on an invoice.

The same drivers apply to the external options; they just show up differently. A good provider has already paid for the search, the general ramp-up and the retention, and sells you only the part you need.

What does an AI engineer embedded in your team look like?

At Sapio we work in the model the industry calls the forward deployed engineer: a senior engineer who works inside your company, on your systems, with your people, and owns one use case until it runs in production and your team uses it every day.

How it works with us:

• We start with a short, paid audit. We pick the use case, check the data on real extracts and tell you in writing whether it is worth doing, including when the answer is "not yet".
• One target, written down at kickoff: what "in production" means and which adoption number we measure.
• Vlad Tudor, Sapio's founder, leads every deployment. He sets the target with your sponsor and owns adoption.
• One of your engineers works alongside ours from week one. At the end you keep the code, the documentation and the evaluation set, and a person who knows them.

For many companies this is also the best way to learn which AI engineer to hire later. After the first months you know what data you have and what profile you need, and someone inside already knows the system. If you want to talk through the model for your case, get in touch.

What we draw on: our team built ai-aflat.ro, the AI assistant for Romanian legislation, which answers from more than 220,000 legislative acts, updated daily, and a ChatGPT-integrated customer-support agent for a retail client that went on to raise a $4.7M seed round. What we build is described on our custom AI agents and AI process automation pages.

How do I decide which option fits?

Answer in order. The first "no" points to your option.

1. Can I name the use case and the person in the company who owns it? If not, start with choosing your first AI use case, not with a person.
2. Do I know where the data is, and can I get access within two weeks? If not, an audit before any hire or contract.
3. Do I have a technical team an AI engineer can work in and hand over to? If not, an embedded engineer, with one of your people alongside.
4. Do I have at least three use cases for the next two years? If not, buy the first project delivered into production, not a headcount.
5. If every answer is yes, hire, and use outside help only for capacity.

"Should I hire an AI engineer?" usually starts from the assumption that the problem is a missing person. More often the problem is a missing result you can point to. The right person is far easier to hire once the first system is running.

If you want to work out whether your first step is a hire or an engineer inside your team, talk to us.

Sources

• Eurostat, statistical report KS-01-26-009 on AI use in enterprises, 2026 edition, published 26 March 2026 (Table 7; reference years 2024 and 2025)
• Bitkom Research, Arbeitsmarkt für IT-Fachkräfte 2025, January 2026 (German)
• Eurofound, IT sector in focus: evolution of the digital workforce, July 2026