Dedicated machine learning engineers for your offshore team.
Offshore machine learning engineers in Chiang Mai and Bangkok, working full time on your product and managed by us. A machine learning engineer builds and trains models with production in mind from the start.
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 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 oversees delivery, training and coaching, the office and equipment, and HR and payroll. The price below is for a junior Machine Learning Engineer; other levels are priced in your proposal, and how the monthly fee is built explains the arithmetic.
From price
One junior Machine Learning Engineer, full time, everything below included. All 27 things included ››
- Machine learning engineer From USD 3,100 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 a Machine Learning Engineer does on a dedicated 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, two common silent failures in model training.
Machine Learning Engineer seniority levels.
Junior to senior. Part of the value is knowing when a simpler method beats machine learning. Engineers at the higher levels make that call faster. Keep that in mind when you look at 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, and both come from MLOps engineering at Azendo. It suits companies that hire dedicated developers for a model that has to work in production as well as in a notebook.
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.
A remote Machine Learning Engineer's first three months on your team.
- 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. Machine learning is the right choice when patterns are too complex for rules and there is enough good data. The engineer tests that against your real data before you commit.
How much data do we need?
It depends on the problem. The engineer we assign checks feasibility against your real data before scoping. They look at how much there is, how clean it is and whether it contains the signal you need. Where data falls short, they suggest ways to collect more or approaches that need less.
Add remote machine learning engineers to your team.
Tell us the problem you want a model to solve, the data you have and how you'd measure success. We'll suggest the level of ML engineer for it and send the fixed monthly fee. A service delivery manager can join the first call.
Tell us what your machine learning engineer would work on.
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