Hire MLOps engineers so models ship and stay healthy

Data scientists should not be copying model files to servers by hand. Cubehire MLOps engineers build the pipelines that train, test, register, deploy and monitor your models automatically, so a new version reaches production in hours and a bad one rolls back on its own.

Hire MLOps Engineers How we vet them
The work

What our MLOps Engineers build

Typical projects companies bring a MLOps engineer in for.

1

Training pipelines

Scheduled and triggered training runs with tracked data versions, parameters and results.

2

Model registry and CI/CD

Automated tests and promotion from staging to production for every model version.

3

Serving infrastructure

Scalable real time and batch inference on Kubernetes or managed endpoints with autoscaling.

4

Drift and performance monitoring

Alerts when input data or prediction quality shifts, before customers notice.

Skills

Skills we test every MLOps engineer on

  • MLflow
  • Kubeflow or Airflow
  • Docker
  • Kubernetes
  • AWS SageMaker
  • Vertex AI
  • Terraform
  • GitHub Actions
  • Feature stores
  • Model monitoring
  • GPU scheduling
  • Python
Vetting

How we vet MLOps Engineers

Every MLOps engineer sits the Cubehire AI interview: multiple choice fundamentals, a timed written and coding round, and a recorded video round. A person on our team then reviews the profile before any company sees it. For this role, the interview checks that they can:

  1. Check 1

    Designs a pipeline that retrains a model weekly and blocks a worse model from deploying.

  2. Check 2

    Explains how to serve a model with low latency and control GPU cost.

  3. Check 3

    Sets up monitoring that separates data drift from bugs in the serving code.

Available now

Vetted MLOps Engineers in the network

Each profile carries a scored AI interview and a human review.

See the whole network

Tell us about your MLOps engineer role

New profiles are approved every week. Describe the role and we will send matched, interview scored MLOps Engineers within 48 hours.

Describe the role
How it works

Hire in 4 easy steps

From the first conversation to a signed engineer, without the six-week search in between.

01
Define
Tell us what you need

Describe the role, the stack and the work. No job spec required, a conversation is enough.

02
Discover
Meet the top engineers

Three to five matched engineers within 48 hours, each with their AI interview score attached.

03
Evaluate
Interview with ease

Pick who you want to meet and we handle the scheduling on both sides.

04
Onboard
Hire with confidence

Contracts, USD payroll and compliance handled. You get an engineer, not an admin project.

Terms

Simple terms for US companies

Shortlist in 48 hours

Three to five interview scored profiles that match your stack, delivered within two days.

Hours that overlap yours

A shifted working day agreed during hiring, with daily overlap with Eastern or Pacific time.

Contracts and payroll handled

We carry the paperwork and pay the developer in USD, so you are not opening an entity abroad.

Replacement guarantee

If the fit is not right, we replace the developer at no extra cost within the guarantee period.

Hiring MLOps Engineers

Questions about hiring MLOps Engineers

What companies ask before they hire a MLOps engineer through Cubehire.

Talk to our team
DevOps ships code. MLOps ships code plus data plus trained models, which means tracking data versions, validating model quality before release and watching predictions after release, not just uptime.
Yes. Many now run LLM gateways, prompt versioning, evaluation pipelines and self hosted open models alongside classic model serving.
If retraining or redeploying it is manual and nobody would notice it degrading, a few weeks of MLOps work usually pays for itself. It also makes the second and third model far cheaper to launch.
AWS, Google Cloud and Azure are all covered, as are open source stacks built on Kubernetes, MLflow and Airflow for teams that want to avoid platform lock in.
A flat rate quoted before you interview anyone: a one time fee for full time placements, or a fixed monthly rate for contract work. There is no percentage of salary. See the pricing page for current rates.

Still have a question about Cubehire?

Ask about vetting, pricing, contracts or how the AI interview works. Cubie, our assistant, answers straight away and raises a ticket for the team when it cannot.

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Ready to hire a MLOps engineer?

Tell us the stack and the work. You get matched, interview scored MLOps Engineers within 48 hours.

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