mlops-engineer - Build scalable ML infrastructure and automation pipelines.
Builds ML pipelines, tracks experiments, and manages model registries using MLflow, Kubeflow, cloud platforms, and modern MLOps tools.
Tags
Updated: 2026-09-24Capabilities
What this skill does
- Build scalable ML infrastructure
- Orchestrate ML pipelines
- Track experiments and models
- Manage model registries and versioning
- Provision infrastructure as code
- Deploy ML models to production
- Monitor model performance and drift
- Implement ML CI/CD automation
Inputs
- MLOps goals and system constraints
- Implementation playbook resource file
Outputs
- Actionable implementation steps and verification plans
- ML pipeline and infrastructure configurations
Requirements
- Docker or Kubernetes container runtime
- Cloud platform or infrastructure access
- MLOps tools like MLflow or Kubeflow