ML Platform Engineer assigned to your roadmap.
An ML Platform Engineer builds the shared infrastructure data scientists work on, so each new model does not rebuild the same plumbing.
Assigned under one service agreement with a minimum monthly capacity in hours, run by a service delivery manager in Chiang Mai, Thailand — five hours ahead of Northern Europe.
This role is delivered as part of our MLOps engineering services. Take the role on its own, or the whole discipline as one delivery team.
What an ML Platform Engineer does on an MLOps team.
Skills and scope are agreed before delivery starts, so the first sprint is productive rather than a ramp-up month. The work runs to the same backlog, the same repositories and the same definition of done as your own team's.
ML Platform Engineer skills and technology we assign for
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Building feature stores and training infrastructure shared across teams.
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Standardising experiment tracking, model registry and reproducibility.
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Managing compute and GPU scheduling, where cost escalates fastest.
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Maintaining shared base images and environments so experiments are reproducible across teams.
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Building self-service access so data scientists provision compute without raising a ticket.
Seniority levels we assign.
Senior. Platform work only justifies itself at a certain scale of model portfolio. Seniority is one of the inputs behind the committed monthly capacity written into your agreement.
When an ML Platform Engineer is the right assignment.
Worth assigning once several models are in production and each is managed differently. On an ongoing roadmap this role is most often assigned alongside an MLOps Engineer or a Machine Learning Engineer. All 4 roles in this service line can sit on the same agreement, and capacity moves between them at the monthly cycle rather than requiring a new contract. This capacity is assigned under MLOps engineering rather than sold as a separate engagement.
Questions about assigning an ML Platform Engineer.
When is an ML platform worth building?
Once several models are in production and each is managed differently. Before that, the platform costs more than the duplication.
Can you manage GPU costs?
Yes, through scheduling, right-sizing and spot capacity. GPU spend is where ML infrastructure budgets usually escape.
Add ML platform engineer capacity to your roadmap.
Tell us the scope and the stack. We come back with the profile, the capacity and what the first month looks like, or you can talk to a service delivery manager first.
Assign a ML Platform Engineer to your roadmap.
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