Dedicated MLOps team.
MLOps engineers for deployment, monitoring and retraining, headhunted for your models and assigned to you full time. You set the priorities. We employ, train and run the team from our own offices in Thailand.
What you get and what you pay.
With offshore staffing or staff augmentation, the provider finds a developer 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 setup 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
Tailored teams down to every detail.
We make sure your team is 100% tailored to your needs and expectations. We headhunt MLOps engineers when needed, so we deliver high-performing teams.
- 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 ten disciplines, on the same agreement and with the same service delivery manager.
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.
Can we choose the seniority level?
Yes. Junior to principal MLOps engineers are available, priced by level, and we recommend a level based on the scope you share.
Who manages the MLOps engineers?
We do. It works like staff augmentation, with the management included: our MLOps engineers are assigned to your models full time, and we employ, train and manage them. Our data scientists keep the say over the models.
What if our stack isn't listed?
Tell us what you use. The list shows what we see most often, and we'll confirm the fit for your setup at scoping.
Can we add a second MLOps engineer later?
Yes, at the next monthly cycle.
Who owns the infrastructure and code?
You do, from the first commit. All work happens in your repositories.
Why does a live model need MLOps?
A live model keeps changing even when its code doesn't. Input distributions move, upstream data changes and accuracy slips quietly. Catching that early is what MLOps is for. Many engagements start with models that have no owner. The first job is a baseline, so drift can be measured against something real.
Do your engineers work with our data scientists?
Our engineers work in your cloud account next to your data scientists. The people who trained a model understand its weak spots, so they stay involved.
What if the data feeding the model is the problem?
When the data feeding a model is the real issue, your team fixes the pipelines first and instruments the model after.
Build the whole team around your MLOps engineers.
Most partners add a second discipline within the first year. It joins the same agreement, the same sprints and the same service delivery manager.
Your MLOps engineers 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 → Tell us what MLOps engineering capacity your roadmap needs.
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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