Dedicated ML platform engineers for your offshore team.
Offshore ML platform engineers in Chiang Mai and Bangkok, working full time on your product and managed by us. An ML platform engineer builds the shared infrastructure your own data scientists work on, so each new model reuses the same foundations.
You direct the work. We manage the people day to day, for one fixed monthly fee.
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 area 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 oversees delivery, training and coaching, the office and equipment, and HR and payroll. The price below is for a junior ML Platform Engineer; other levels are priced in your proposal, and how the monthly fee is built explains the arithmetic.
From price
One junior ML Platform Engineer, full time, everything below included. All 27 things included ››
- ML platform engineer From USD 3,000 per month
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Service
We manage and develop the team
- A service delivery manager who oversees operations and 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 team member
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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 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 a dedicated MLOps team.
Your own 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.
Junior to lead. Platform work pays off once you have a portfolio of models to support, and a senior engineer usually designs it. Who designs and who builds is a question of levels, and levels are one of the inputs into how the monthly fee is built.
When your team needs an ML Platform Engineer.
The time to add one is when several models are in production and each is managed differently. The role sits beside the MLOps and machine learning engineers in MLOps engineering at Azendo. ML platform engineers fit companies that hire offshore developers for several models and want them built on shared foundations.
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.
A remote ML Platform Engineer's first three months on your team.
- 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, or several teams are building them in different ways. Before that, simpler tooling and a well-documented deployment path are usually enough. An ML platform engineer turns the repeated work of training, serving and monitoring into shared infrastructure every new model can reuse.
Can you manage GPU costs?
Yes, through scheduling, right-sizing, spot capacity and shutting down idle resources, which is where many ML infrastructure budgets can be brought under control. The engineer puts budgets and usage reporting in place too, so each team sees what its experiments cost and plans GPU time accordingly.
Add remote ML platform engineers to your team.
Tell us how many teams train models, what they share today and where the GPU budget goes. We'll come back with the platform engineer we'd hire and the monthly fee. We need four weeks from signing to hire the right engineer.
Tell us what your ML platform engineer would work on.
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