An MLOps engineer gets trained models into production and keeps them working

The parts peculiar to models. Code fails loudly; a model keeps answering while its predictions get worse, because live data has moved away from what it learned on. The MLOps engineer versions training data, automates retraining, runs the registry and watches prediction quality. Servers, clusters and the delivery pipeline usually stay with a DevOps engineer, and the two work side by side.

Not always. One model retrained a few times a year can live in a well-written pipeline that your ML or DevOps engineer runs. An MLOps engineer pays off when models retrain often, several share a platform, or a wrong prediction costs money before anyone notices it. We say which case you are in before any profiles go out.

No. A managed platform saves effort when your team already works in that cloud. Open-source tools such as MLflow and Kubeflow suit teams that already run their own clusters or need the same set-up on more than one cloud. The recommendation comes after the engineer has seen your models, your request volumes and the people who will maintain it.

It depends on how fast the world behind the data moves. A model scoring transactions can go stale in weeks, while one sorting documents may hold for a year. The engineer starts with a cautious schedule, then stretches or shortens it as monitoring shows how quickly quality decays, and adds a drift threshold that can start a run early.

Yes. The engineer first tries to rebuild each model from its recorded data and code, and compares the predictions with what is live. A model that cannot be reproduced is documented as it is and left serving until it has been rebuilt properly, never retrained blind. You get a written list of which models can be reproduced and which cannot.

By matching hardware to traffic. The engineer measures cost per thousand predictions, moves scoring that does not need real time to batch jobs, tests smaller or compressed model formats against the accuracy the product needs, and lets GPUs scale down between peaks. Each saving is checked on the evaluation set, because a cheaper model that answers worse is not a saving.

Your team sets the rule; the engineer enforces it. Before the first rollout, the team whose product uses the predictions agrees what a candidate must beat and how long it runs as a canary. The MLOps engineer automates that check and holds anything that fails it. Overriding the rule is a person’s decision, and it is written down.

Yes, when hosting them yourself makes sense. The engineer sets up an inference server, sizes GPU memory and batching for your traffic and puts the same registry, canary and monitoring around it as any other model. Before you buy or rent GPUs, both options are costed per request on your real traffic, because at low volume a hosted API often costs less.

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.