Dedicated MLOps team.
Offshore MLOps, machine learning and ML platform engineers work only on your models, as a dedicated MLOps team. We hire each one for the platform you run and employ them in our offices in Bangkok and Chiang Mai, Thailand. They deploy, monitor and retrain what your own data scientists build. You decide what good enough looks like.
What you get and what you pay.
An MLOps engineer hired through offshore staffing or staff augmentation reports only to you. Ours also have a service delivery manager, who reviews delivery with you every other week, and a Talent Success manager who coaches them on a quarterly plan. Azendo employs them and provides the office, the hardware and the HR.
Price examples
Junior level, full time, everything below included. Mid-level and senior levels are priced in your proposal. All 27 things included ››
- MLOps engineer From USD 3,000 per month
- Machine learning engineer From USD 3,100 per month
- ML platform 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 costs each month.
You agree a monthly capacity in hours with us. The fee covers the MLOps engineer's salary and every layer around it: the service delivery manager who oversees delivery, training and development, the workplace, the equipment and the HR behind the employment. It is the same amount every month.
- One agreement and one invoice, with no upfront fee or management surcharge
- Holidays are already priced in, so April and May cost the same
- A capacity report every month, so you see the hours delivered
MLOps engineers hired for the platform you run.
Serving, tracking and pipeline tools differ a lot between companies, so we search for yours. Each candidate goes through our assessment before you see them.
- Roles and seniority
- Stack and domain
- Way of working
- Capacity and time to hire
Talk to our teamBook a scoping call - 25 Week 1 Sourcing Headhunted for your brief
- 12 Week 2 AI processing and screening Matched to your stack
- 5 Week 3 Technical testing Pass technical testing
- 1 Week 4 Mindset, culture and logic Proposed to you
Roles in an MLOps team.
Deployment, platform, machine learning and MLOps roles have pages of their own. As more models reach production, data scientists and cloud engineers can join the team.
What a dedicated MLOps team keeps running.
A model that worked at launch can drift as your data changes, and the first sign is often a business number that moves. A dedicated MLOps team keeps the models you run in production accurate and affordable: automated pipelines, monitoring and drift detection, retraining on a schedule and a clean path from experiment to deployment. The MLOps engineers we assign work in your cloud account next to your own data scientists, and they start from a measured baseline so every change can be judged against something real.
- Model deploymentServing on Kubernetes with KServe, Seldon or Triton, with versioned releases and rollback.
- Training and retraining pipelinesAutomated pipelines that retrain on a schedule or when the data shifts.
- Monitoring and drift detectionAccuracy, drift and latency tracked against the baseline, with alerts.
- Experiment trackingRuns, data versions and results in MLflow or Weights & Biases, so any model can be reproduced.
- Infrastructure as codeTraining and serving environments defined in Terraform and kept in your repositories.
- Model documentationWhat each model does, what it was trained on and how to retrain it.
Stacks we work in
We hire for the platform you run. If yours is built on tools that are not listed, those tools become the search brief.
- Kubernetes
- Docker
- KServe
- Seldon
- TorchServe
- Triton
- MLflow
- Weights & Biases
- DVC
- Neptune.ai
- Comet
- GitHub Actions
- Airflow
- Terraform
- CircleCI
- Argo Workflows
What a dedicated MLOps team is.
A dedicated MLOps team is a group of engineers who take one company's models from notebook to production and keep them healthy, full time: deployment, monitoring and retraining. Your own data scientists keep building the models. Azendo employs the MLOps engineers and answers for delivery, every week.
Where it pays off most
It pays off when models work in notebooks but reach production slowly, or drift once they are there. With deployment, monitoring and retraining handled by one team, your own data scientists spend their time on the models, and every model ships through the same tested path.
Who is on the team
- MLOps engineerDeployment, monitoring and retraining
- Machine learning engineerNotebook to production service
- ML platform engineerFeature stores and training platform
- Model deployment engineerServing, versioning and rollback
- Service delivery managerOversees delivery on our side and reviews it with you every two weeks
- Talent Success managerFor every specialist: a quarterly development plan and coaching tailored to it
- Your product ownerSets the priorities and accepts the work
Other ways to build an MLOps team, compared.
| Dedicated team, Azendo | Staff augmentation | Project outsourcing | |
|---|---|---|---|
| What you buy | Agreed monthly capacity from people who work only on your product | A person's hours, billed by the hour or the month | A defined scope, at a fixed price or on time and materials |
| Who manages the team | Azendo, day to day, with you setting the priorities | You do, day to day: Azendo's estimate is four to eight hours a week per specialist | The vendor's project manager |
| Who answers for delivery | Azendo, scored by you every Friday | You do | The vendor, against the agreed scope |
| How long people stay | On your product, and with Azendo for 3.3 years on average | Until the contract ends | Until the project is handed over |
| Best fit | Models that have to be deployed, watched and retrained for years | A short gap in a team you already run | One project with a clear end |
Try us, at zero risk.
- No deposit
- No upfront fee
- No commitment
We agree a time to hire with you. If we can't find the right people for your team within it, you walk away with no commitment and nothing to pay.
Get in touch"We are 100% committed in a partnership. We go above and beyond, which is why we can say that it is truly a managed service we offer."
Mikkel Schmidt, CEO and founder
Questions about a dedicated MLOps team.
What does a dedicated MLOps team do?
A dedicated MLOps team runs your machine learning models in production full time: deployment, pipelines, monitoring, drift detection and retraining. With Azendo the engineers are ours, managed from Chiang Mai and Bangkok, and they work in your cloud account alongside your own data scientists.
How much does a dedicated MLOps team cost?
MLOps, ML platform and model deployment engineers start from USD 3,000 per month at junior level, full time, and machine learning engineers from USD 3,100. The fee also covers delivery management, a Talent Success Manager and training, the office and equipment, and HR and payroll. See how the monthly fee is built.
When does a company need MLOps?
When models run in production and someone has to keep them right. Typical signs are models that only one person can retrain, accuracy that is no longer tracked after launch and deployments that take days of manual work.
Do MLOps engineers replace our data scientists?
No. Your own data scientists keep building and improving models. The MLOps engineers build the platform those models run on and take over deployment, monitoring and retraining, so the data scientists spend their time on the models.
Which tools does the team work with?
Kubernetes, Docker, KServe, Seldon and Triton for serving, MLflow, Weights & Biases and DVC for tracking, and Airflow, Argo Workflows and Terraform for pipelines. The engineers start from what you run today and add tools only where a gap is costing time or accuracy.
Can you take over models that are already in production?
Yes. The engineers document what is running, put monitoring on it and measure a baseline first, so you know how each model performs today. Then they automate deployment and retraining one model at a time, starting with the one the business depends on most.
How does MLOps fit with DevOps and data engineering?
MLOps sits between them. It uses the same infrastructure practices as DevOps and depends on the pipelines data engineering builds. All three can run under one agreement, with one service delivery manager, so a change in the data or the platform reaches the people who run the models straight away.
Do the MLOps engineers work remotely?
Yes. Each one is employed by Azendo and assigned to your models full time, working in your cloud account, your ML platform and your monitoring. Their week is mostly in our offices in Bangkok and Chiang Mai, with up to two days from home on a secure VPN.
Service areas that often join an MLOps team.
Each role joins under the same agreement, works in the same sprints and reports to the same service delivery manager. You add one when your product calls for it.
MLOps team MLOps Engineer, Machine Learning Engineer, ML Platform Engineer, Model Deployment Engineer. The core of the team. On the team 
AI engineering LLM features, retrieval and evaluation Add to the team → 
Data engineering Pipelines, warehouses and reporting models Add to the team → 
Cloud and DevOps Infrastructure as code, CI/CD, observability Add to the team → 
Data science Analysis, models and evaluation Add to the team → Let's scope your MLOps team.
Thirty minutes with a service delivery manager. You leave with a clear picture of the team you would need, what it would deliver and how we would hire it in four weeks.
- No deposit and no upfront fee
- No commitment until the right people are on your team
- If we can't find them inside the agreed window, you walk away at no cost
Mikkel Schmidt and John Mark, the people you would talk to. No slides, no sales script. Loading the form…
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