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 delivered as part of our MLOps engineering services. Take the role on its own, or the whole discipline as one delivery team.
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 your engineer will recommend it when it does.
How much data do we need?
It depends on the problem. Your engineer 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.
Loading the contact form… You can also email hello@azendo.co.
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