Hire an MLOps Platform Engineer for your team.
Builds a shared platform for training, registering and deploying models across teams. Available full-time, part-time, hourly or by project.
- 7-day risk-free trial
- NDA and IP protection
- Onboard in 48 hours
Tell us who you need.
What an MLOps Platform Engineer does for you.
Builds a shared platform for training, registering and deploying models across teams. Unlike an MLOps engineer serving one team, this person builds shared infrastructure that many data science teams can use.
Core skills
- Kubeflow
- Model registry
- Feature stores
- Platform automation
- Build serving, retrieval and training platforms
- Control GPU, inference and storage costs
- Secure models, data and prompts
- Add monitoring, governance and evaluations
When this role makes sense.
AI usage is growing across teams
Security or compliance review is due
Cost and reliability are becoming problems
Ready to hire an MLOps Platform Engineer?
Share your requirements and get matching profiles within 24 hours.
Three simple steps, no hiring fees.
Share your requirements
Tell us the role, the work and your goals. We share matching profiles within 24 hours.
Interview and select
Interview the people you like. Technical rounds and trial tasks are up to you, at no charge.
Onboard and start
Sign the NDA and the person joins your tools and meetings, usually within 48 hours.
Flexible terms, low risk.
7-day risk-free trial
Not satisfied in the first 7 business days? Get a replacement or end it at no charge.
NDA and IP protection
We sign both before work starts, and you own everything that is produced.
Free replacement
If the fit is wrong, we arrange a replacement with a structured handover.
Easy exit
Scale down or stop with 15 days’ written notice.
Looking for a different role?
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Still unsure? Talk to a strategist.
- Profiles within 24 hours
- NDA signed before any discussion
- 7-day risk-free trial
What does an MLOps Platform Engineer do?
Builds a shared platform for training, registering and deploying models across teams. Typical work includes: build serving, retrieval and training platforms; control GPU, inference and storage costs; secure models, data and prompts; add monitoring, governance and evaluations.
How do we evaluate an MLOps Platform Engineer?
Ask how they got teams to adopt a shared model registry or feature store. They should balance standardization with flexibility, and offer sensible defaults.
What skills should an MLOps Platform Engineer have?
Look for hands-on experience with Kubeflow, Model registry, Feature stores, Platform automation, plus clear communication and evidence of similar work.
When should we hire an MLOps Platform Engineer?
This role is a good fit when: aI usage is growing across teams; security or compliance review is due; cost and reliability are becoming problems.
How quickly can an MLOps Platform Engineer start?
We share matching profiles within 24 hours. After you interview and choose, onboarding usually takes 48 hours.
Who owns the work?
You do. We sign an NDA and an IP assignment agreement before work starts, and everything produced belongs to your business.
What if the person is not the right fit?
Every hire starts with a 7-day risk-free trial. After that, you can request a replacement or end the engagement with 15 days’ written notice.