A generative AI engineer answers for what the model produces

No. The prompt is one stage of five. A generative AI engineer also writes the output spec, builds the test set, adds schema and safety checks in code, runs the editor queue, and answers for the result once it ships. When output fails because the model cannot reach your data or tools, that is AI engineer work, and we say so.

It is rarely the first step. Most output problems are fixed faster with a clearer spec, better examples and a test set, and those have to exist anyway to show whether fine-tuning helped. When a hosted model still falls short on the test set, training becomes work for an ML engineer, scoped on its own.

Against a test set of your real inputs. Where there is one right answer, such as an extracted date, it is written down and compared on every run. Where there is not, such as tone or usefulness, people grade a sample against a rubric, and a model grader is trusted only once its grades match theirs. Those grades decide releases.

The design makes it rarer and easier to catch. The model writes only from material it is given and points to the passage behind each claim, and a check confirms the cited text exists. When the source does not support a result, none is produced. No prompt removes the risk entirely, so the check runs on every output, not only the test set.

Your content owner, in writing. The engineer drafts the rules: topics the model declines, claims it may not make, personal data it must not repeat. Your content owner or legal team signs them off, the checks enforce them on every release, and a change to any rule goes back through the same sign-off.

Yes, with hosted models that read images and documents or transcribe speech, and the same discipline as text: a spec for the output, a test set of your real files and a check on every result. Where a model is unsure, such as on a blurred scan, the result goes to a person instead of being guessed. Training a vision or speech model of your own is ML engineer work.

Ask about one output they answered for: the spec they wrote, how they tested it and what they changed when it failed in production. Then hand them ten real outputs from your product, two of them wrong, and ask how they would catch those two automatically. Strong candidates ask for the spec before they talk about prompts.

In your repository from the first day, versioned with the code that calls them. Each release records which prompt, model and test results went out together, so whoever inherits the work can see why the output looks the way it does and can roll back to an earlier version.

Yes. Many clients start with one senior engineer to cover a specific capability or capacity gap, then add specialists only when the roadmap requires them.

Yes. Adding an engineer follows the same route as the first, and you interview them. Reducing the team follows the notice period agreed in the contract, and the work is handed over in your repository before anyone leaves.

Yes. You manage priorities and day-to-day work directly, in your own channels and delivery process.

Yes. You interview every candidate using your own process and make the final decision about who joins the team.

Our engineers are ImproveIT employees and long-term contractors working through our teams in Poland and Ukraine. We are not a freelance marketplace and do not resell developers from other vendors.

Replacement terms are defined in the contract upfront, so it does not become a negotiation.

You do. IP assignment is included in the contract, and we can sign an NDA before discussing the project.

We sign an NDA before discussing the project, and confidentiality and IP assignment are part of the contract. Engineers work in your repositories, cloud accounts and tools with the access your team grants, so you can narrow or revoke it at any time.

Yes. Our engineers work with teams across Europe and the US. We agree the required working-hour overlap before matching candidates.

Our developers work in English with international product and engineering teams every day. English level is included in the candidate information you receive before the interview.

Rates depend on the role, seniority, experience, and engagement model. You receive the rate, availability, and candidate profile in writing before making any commitment.

Both are agreed before the engagement starts and included in the proposal and contract. We are flexible in that and usually agree on the terms suitable for a client.