From the requirements call to a model tracked against real metrics. Here is how we vet, match and onboard every machine learning engineer we place.
Before an engineer joins our bench, they are tested on data preparation, feature engineering, model evaluation, and a hands-on task training and scoring a model against a realistic dataset. Only engineers who pass are shortlisted to clients.
We match engineers to your specific problem: the type of prediction you need, the data you have, and the timezone overlap that works for your team. You see 2 to 3 fitting profiles, not fifty.
The engineer reviews your data, agrees the success metrics with you, and starts on the first model in the first week. You manage priorities directly from there.
Works only on your account, not shared across other clients' models.
Access to the engineer on your own tools, with no account manager relay.
Full time, part time, or a fixed scope, adjustable with notice as your roadmap changes.
Performance reviewed against the metrics agreed before training started.
Working hours arranged to overlap with your team for stand-ups and reviews.
A new engineer at no extra cost if the current one is not the right fit.
Tell us what you need predicted or classified, and we will shortlist engineers who fit your data and your timezone.
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