Dedicated MLOps team.
MLOps, machine learning and ML platform engineers work only on your models, as a dedicated MLOps team. Azendo employs, trains and manages them from its own offices in Chiang Mai and Bangkok, 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.
With offshore staffing or staff augmentation, the provider finds an MLOps engineer and the managing is left to you. We do it as a full service. The MLOps engineers are ours: we employ them, assign them to you full time, give them an office and equipment, train and coach them, and run delivery with you every week.
Price examples
Mid-level, full time, everything below included. Junior and senior levels are priced in your proposal. All 27 things included ››
- MLOps engineer From USD 4,200 per month
- Machine learning engineer From USD 4,200 per month
- ML platform engineer From USD 4,200 per month
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Service
We manage and develop the team
- A service delivery manager who runs the team and answers for 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 specialist
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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 own 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
One monthly fee. Everything around the MLOps engineer is in it.
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 runs the team, 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
- Capacity moves up or down at the next monthly cycle
Your MLOps team, tailored down to every detail.
We select every person for your stack, your product and the way your team works. When the right MLOps engineers are not already with us, we headhunt them and test them ourselves before you meet them.
- Roles and seniority
- Stack and domain
- Way of working
- Capacity and start date
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
Any role your roadmap needs.
These four are the MLOps engineering roles with their own pages. Beyond them we assign specialists across eleven disciplines, on the same agreement and with the same service delivery manager.
What a dedicated MLOps team works on.
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
- Kubernetes
- Docker
- KServe
- Seldon
- TorchServe
- Triton
- MLflow
- Weights & Biases
- DVC
- Neptune.ai
- Comet
- GitHub Actions
- Airflow
- Terraform
- CircleCI
- Argo Workflows
Matched to what you run today, agreed at scoping.
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 managerRuns the team on our side and answers for delivery
- Talent Success managerFor every specialist: a development plan and coaching every month
- 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 work | A service delivery manager, 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, every week | 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 | A product that keeps growing, with a roadmap that runs 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 don't hand you an MLOps engineer and step back. We build the team around your product, and we stay in it every week."
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, machine learning, ML platform and model deployment engineers start from USD 4,200 per month at mid-level, full time. The fee covers the engineer and everything around them: delivery management, a Talent Success Manager and training, the office and equipment, 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.
Disciplines that join an MLOps team.
Each discipline joins under the same agreement, works in the same sprints and reports to the same service delivery manager. You add one at the next monthly cycle, when the roadmap 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 when it could start.
- 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
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