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Codemotives

AI & Data Talent

Hire MLOps Engineers

Our MLOps engineers build the pipelines and infrastructure that move models from experiments to production, and keep them accurate and observable once live.

Get in touchHow we work

Overview

Hire MLOps Engineers

MLOps engineers bring software delivery discipline to machine learning. They automate data and training pipelines, version models and datasets, deploy through tested release processes and monitor predictions in production. This lets data scientists focus on modeling while the platform handles repeatability. You stay informed throughout, with clear explanations of what is being done and why it matters.

We work with your existing cloud and tooling rather than forcing a stack, and we keep the setup proportionate to your needs; a small team often needs less machinery than a large one. Engagements can be dedicated, part-time or project-based, with documentation your team can run without us. We would rather raise a concern early than let it become an expensive surprise later in the project.

Context

When to hire

  • 01

    Models are deployed by hand

    Releases rely on one person and a set of manual steps, which is slow and risky.

  • 02

    Nobody knows when a model degrades

    Without monitoring, accuracy problems surface only when users complain.

  • 03

    Experiments cannot be reproduced

    Data, code and parameters are scattered, so results are hard to repeat or audit.

  • 04

    Compute costs are unclear

    Training and serving bills grow without visibility into what drives them.

Capabilities

Skills you get

  1. 01

    Training pipelines

    Orchestrate data preparation, training and validation as repeatable, scheduled workflows.

  2. 02

    Model registry and versioning

    Track models, datasets and parameters so any release can be traced and rolled back.

  3. 03

    CI/CD for ML

    Test data, code and models automatically before they reach production.

  4. 04

    Serving infrastructure

    Deploy models as scalable services or batch jobs on containers and cloud platforms.

  5. 05

    Monitoring and drift detection

    Watch latency, errors, input drift and prediction quality, with alerts.

  6. 06

    Cost management

    Right-size compute and use autoscaling to keep spend predictable.

Typical work

  • Automating model retraining
  • Containerizing model serving
  • Setting up experiment tracking
  • Building model monitoring dashboards
  • Migrating ML workloads to the cloud

Tools they use

  • MLflow
  • Kubeflow
  • Airflow
  • Docker
  • Kubernetes
  • Terraform
  • Prometheus
  • GitHub Actions

Approach

How hiring works

  1. 1

    Share your needs

    Tell us about your project, stack, team setup and the skills you are looking for.

  2. 2

    Review the match

    We suggest developers whose experience fits, and you can talk with them before deciding.

  3. 3

    Onboard

    Access, tools, communication channels and working hours are agreed up front.

  4. 4

    Build & review

    Work runs in your process with regular updates, and the team can scale as needs change.

FAQs

Common questions

Have a question that is not covered here? Get in touch.

Do we need MLOps if we only have one model?

Possibly a lightweight version. Even one production model benefits from versioning, tests and monitoring, and we scale the setup to what you actually need.

Can you work with our current cloud provider?

Yes, we adapt to your existing platform and tools where practical, and discuss alternatives only if there is a clear reason.

Will our team be able to run it afterward?

That is the goal. We document pipelines and runbooks and can walk your team through them.

How do engagements work?

Dedicated, part-time and project-based models can be discussed. We agree on reporting and review rhythm at the start.

Explore

Keep exploring

Let's build what's next

Tell us about your idea, product or legacy system. Send us a message and we will help you scope the right next step.

Get in touch