MLOps Engineer assigned to your roadmap.
An MLOps Engineer gets models out of notebooks and into production, then keeps them working as the data underneath them shifts.
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 an MLOps 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.
MLOps Engineer skills and technology we assign for
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Building deployment pipelines for models, with versioning and rollback.
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Monitoring for drift and performance decay, which is the failure mode unique to machine learning.
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Automating retraining so model quality does not depend on someone remembering.
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Building reproducible training pipelines so a model can be rebuilt from scratch when needed.
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Managing the model registry so it is always clear which version is serving production traffic.
Seniority levels we assign.
Mid to senior. This role sits between data science and infrastructure and needs credibility in both. Seniority is one of the inputs behind the committed monthly capacity written into your agreement.
When an MLOps Engineer is the right assignment.
Assigned where models exist but nobody owns them after deployment. On an ongoing roadmap this role is most often assigned alongside a Machine Learning 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. The work is delivered as part of MLOps engineering at Azendo.
Questions about assigning an MLOps Engineer.
Our models are in notebooks. Where do we start?
With one model and a deployment path. Trying to industrialise everything at once is how these projects stall.
How do you detect model drift?
By monitoring input distributions and output quality against a baseline, with alerting thresholds agreed with your team.
Add MLOps 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 MLOps Engineer to your roadmap.
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