Dedicated ML platform engineers for your offshore team.
Offshore ML platform engineers in Chiang Mai and Bangkok, working full time on your roadmap and managed by us. An ML platform engineer builds the shared infrastructure your data scientists work on, so each new model reuses the same foundations.
One agreement and one fixed monthly fee, with a service delivery manager in Thailand who runs your team day to day.
This role is part of a dedicated MLOps engineering team. It's staff augmentation, fully managed: take one role on its own, or the whole discipline as one delivery team.
One ML Platform Engineer, assigned to your product and fully managed.
We assign an ML Platform Engineer to your team full time and run everything around them: a service delivery manager who answers for the work, training and coaching, the office and equipment, and HR and payroll. The price below is for a mid-level ML Platform Engineer; junior and senior levels are priced in your proposal, and how the monthly fee is built explains the arithmetic.
From price
One mid-level ML Platform Engineer, full time, everything below included. All 27 things included ››
- ML platform engineer From USD 4,200 per month
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Service
We manage and develop the team
- A service delivery manager who runs the team and answers for delivery
- A 1:1 with our Head of Delivery every two weeks
- Weekly delivery scoring and monthly capacity reports
- A Talent Success Manager for every specialist
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HR
We are the employer
- Recruitment and technical assessment
- Employment contracts
- Salary and payroll
- Tax and social security
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Facilitation
We provide the workplace
- A desk in our own office in Chiang Mai or Bangkok
- A Workplace Experience Manager on site
- Team events through the year
- IT support
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Equipment
We supply the tools
- Laptop and hardware
- Software licences
- LinkedIn Learning access
- Device security and management
What an ML Platform Engineer does on an MLOps team.
Your data scientists are their users. They standardise how each team trains, tracks and deploys, in your cloud account, and cut the time to a new model.
ML Platform Engineer skills and technologies
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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.
ML Platform Engineer seniority levels.
Senior. Platform work pays off once you have a portfolio of models to support. Seniority is one of the inputs into how the monthly fee is built.
When your team needs an ML Platform Engineer.
Worth adding once several models are in production and each is managed differently, alongside an MLOps or machine learning engineer. ML platform engineers join as part of MLOps engineering at Azendo.
What we look for in a dedicated ML Platform Engineer.
We look for ML platform engineers who have run GPU workloads and know where the cost goes. They should standardise experiment tracking and environments, and give data scientists self-service access they will use.
Your remote ML Platform Engineer's first three months.
- Weeks 1 to 2Reviewing how each team trains, tracks and deploys models today.
- Weeks 3 to 6Standardising experiment tracking, the model registry and shared environments.
- Months 2 to 3Building self-service compute and a feature store where several models need it.
Questions about adding an ML Platform Engineer to your team.
When is an ML platform worth building?
Once several models are in production. Before that, simpler tooling is usually enough.
Can you manage GPU costs?
Yes, through scheduling, right-sizing and spot capacity, which is where most ML infrastructure budgets can be brought under control.
Add remote ML platform engineers to your team.
Tell us your roadmap and your stack, and we'll come back with the right profile, the capacity and a start date. You can also talk to a service delivery manager first.
Add ML platform engineers to your remote team.
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