An AI engineer puts models to work inside the systems you already run

By where the work sits. An AI engineer connects the model to your product: the documents it searches, the tools it calls, whose permissions apply and what happens when a provider fails. A generative AI engineer answers for the content the model produces: its format, its quality and what it must refuse. Wrong data or a broken call points here; a well-sourced answer that reads badly points there.

Only if a model has to be trained on your own data. Most AI features call a hosted or open-weight model, and the hard part is everything around that call: retrieval, tools, access and cost. When no available model does the task well enough, an ML engineer trains one, and the AI engineer still connects it to your product.

Yes. The engineer starts from the provider and keys you have, whether OpenAI, Gemini, Cohere or an open-weight model you host, and first moves the calls behind one interface in your code. A later switch then becomes a configuration change and a run of the evaluation suite, not a rewrite.

Only what the request code sends, so the engineer makes that code the control point. Fields the model does not need are stripped or masked before the call, each payload is logged on your side, and the provider account's data-use and retention settings are checked and written down. If a system's owner says a field may never leave, it never enters a prompt.

Yes, through tools the model calls with typed arguments that your code validates before anything runs. A tool that changes records gets a dry-run mode, a key that stops a retry applying it twice, and a log line naming the user it acted for. Whether a person confirms each action is decided tool by tool, with you.

With a set of real questions whose right answers and source documents are known. Every change to a prompt, model, chunking rule or index runs against it, and the build fails if the right document stops coming back or an answer loses its citation. Questions users flag in production are added to the set, so it grows with use.

For a first feature, often yes: retrieval, tools, evaluation and release fit one senior engineer. Some of what surrounds it does not. Training a model of your own, running GPU servers for a self-hosted model, or writing content rules with your legal team are separate jobs, and we say which of them you need before the profiles go out.

Ask them to walk you through an AI feature they put in front of real users: where its answers came from, what broke in the first month and how they found out. Then give them one of your documents and a question it answers, and ask how they would stop a user without access from seeing it. Strong candidates raise access and failure before prompts.

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.