Dedicated MLOps engineers for your offshore team.
Offshore MLOps engineers in Chiang Mai and Bangkok, working full time on your product and managed by us. An MLOps engineer gets models out of notebooks and into production, and keeps them accurate as the data underneath them changes.
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 MLOps Engineer, assigned to your product and fully managed.
We assign an MLOps 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 MLOps Engineer; other levels are priced in your proposal, and how the monthly fee is built explains the arithmetic.
From price
One junior MLOps Engineer, full time, everything below included. All 27 things included ››
- MLOps engineer From USD 3,000 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 an MLOps Engineer does on a dedicated MLOps team.
They work between your own data scientists and your infrastructure team, in your cloud account, and own the path from trained model to production.
MLOps Engineer skills and technologies
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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.
MLOps Engineer seniority levels.
Junior to senior. The role spans data science and infrastructure and needs credibility in both. That breadth takes years to build, so think about the level early. It is part of how the monthly fee is built.
When your team needs an MLOps Engineer.
The right addition when models exist and have no owner after deployment. Often paired with a machine learning or ML platform engineer, the neighbouring roles in MLOps engineering at Azendo. Companies that hire remote developers for machine learning add MLOps once the first model needs to stay accurate in production.
What we look for in a dedicated MLOps Engineer.
We look for MLOps engineers who are credible with data scientists and infrastructure teams alike. They should build reproducible pipelines, watch for drift, and automate retraining so it runs on schedule.
A remote MLOps Engineer's first three months on your team.
- Weeks 1 to 2Reviewing your models, training code and deployment path.
- Weeks 3 to 6Building a deployment pipeline with versioning, rollback and a model registry.
- Months 2 to 3Adding drift monitoring and automated retraining for models in production.
Questions about adding an MLOps Engineer to your team.
Our models are in notebooks. Where do we start?
With one model and one deployment path: versioned code and data, automated training, tests and a monitored endpoint. Once that path works end to end, the next models reuse it. Starting small proves the approach on something real and avoids building a platform before you know what it needs.
How do you detect model drift?
By monitoring input distributions and output quality against a baseline, with alert thresholds agreed with your team. When drift crosses a threshold, the engineer investigates the cause and retrains or adjusts the model. Every model version is tracked, so you can always see what changed and when.
Add remote MLOps engineers to your team.
Tell us how many models you run, where they are deployed and who looks after them now. We'll come back with the MLOps profile we'd hire and the monthly fee. Our time to hire for an MLOps engineer is four weeks from signing.
Tell us what your MLOps engineer would work on.
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