Machine Learning Engineer assigned to your roadmap.
A Machine Learning Engineer builds and trains the models themselves, with production constraints in mind from the start rather than bolted on later.
Assigned under one service agreement with a minimum monthly capacity in hours, run by a service delivery manager in Chiang Mai, Thailand — five hours ahead of Northern Europe.
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.
Skills and scope are agreed before delivery starts, so the first sprint is productive rather than a ramp-up month. The work runs to the same backlog, the same repositories and the same definition of done as your own team's.
Machine Learning Engineer skills and technology we assign for
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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.
Seniority levels we assign.
Mid to senior. Knowing when not to use machine learning is part of the value. Seniority is one of the inputs in how the monthly fee is built.
When a Machine Learning Engineer is the right assignment.
Pairs with an MLOps Engineer where the model needs to run continuously. On an ongoing roadmap this role is most often assigned alongside an MLOps Engineer or an ML Platform Engineer. All 4 roles in this service line can sit on the same agreement, and capacity moves between them at the monthly cycle rather than requiring a new contract. At Azendo this sits inside MLOps engineering, on the same agreement as the rest of the team.
Questions about assigning a Machine Learning Engineer.
Do we actually need machine learning for this?
Often not. A rules engine or a simpler statistical approach solves a surprising share of what gets framed as an ML problem, and we will say so.
How much data do we need?
It depends entirely on the problem. We assess feasibility against your actual data before scoping anything.
Add machine learning engineer capacity to your roadmap.
Tell us the scope and the stack. We come back with the profile, the capacity and what the first month looks like, or you can talk to a service delivery manager first.
Assign a Machine Learning Engineer to your roadmap.
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