Dedicated machine learning engineers for your offshore team.
Offshore machine learning engineers in Chiang Mai and Bangkok, working full time on your roadmap and managed by us. A machine learning engineer builds and trains models with production in mind from the start.
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 Machine Learning Engineer, assigned to your product and fully managed.
We assign a Machine Learning 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 Machine Learning 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 Machine Learning Engineer, full time, everything below included. All 27 things included ››
- Machine learning 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 a Machine Learning Engineer does on an MLOps team.
They work with your data and product teams, agree how success is measured before training starts, and hand models over ready for your MLOps setup.
Machine Learning Engineer skills and technologies
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Feature engineering and training pipelines against your own data.
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Model selection and evaluation, including knowing when a simpler model is the correct answer.
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Packaging models so deployment is not a separate research project.
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Building the offline evaluation harness before training, so results can be compared meaningfully.
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Handling class imbalance and data leakage, which are the two most common silent failures in model training.
Machine Learning Engineer seniority levels.
Mid-level to senior. Part of the value is knowing when a simpler method beats machine learning. Seniority is one of the inputs into how the monthly fee is built.
When your team needs a Machine Learning Engineer.
Pairs with an MLOps engineer when a model needs to run continuously in production. Machine learning engineers join as part of MLOps engineering at Azendo.
What we look for in a dedicated Machine Learning Engineer.
We look for ML engineers who check for leakage and class imbalance before celebrating a score. They should build the evaluation harness first and suggest a simpler method when it will do the job.
Your remote Machine Learning Engineer's first three months.
- Weeks 1 to 2Exploring your data and agreeing how success will be measured.
- Weeks 3 to 6Building the evaluation harness and first models against a simple baseline.
- Months 2 to 3Packaging the best model for deployment with your MLOps setup.
Questions about adding a Machine Learning Engineer to your team.
Do we need machine learning for this?
Sometimes a rules engine or a simpler statistical method solves the problem better, and our engineer will recommend it when it does.
How much data do we need?
It depends on the problem. The engineer we assign to you assesses feasibility against your real data before scoping.
Add remote machine learning 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 machine learning engineers to your remote team.
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