Senior MLOps Engineer (GCP)

New

SoftetaMLOps platform

Workable workplace: remoteFull-TimeSenior

Salary not disclosed

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Job Details

Experience: 5+ years of experience in MLOps or platform engineering, including at least 2 years of end-to-end ownership of production ML infrastructure.
Required Skills: PythonKubernetesAirflowCI/CDBigQuery

Have 5+ years of experience in MLOps or platform engineering.
Have at least 2 years of end-to-end ownership of production ML infrastructure.
Bring strong production experience with Vertex AI, GKE, and BigQuery.
Have in-depth Kubernetes cluster operation and troubleshooting skills.
Have experience with Kubeflow Pipelines or Vertex AI Pipelines, alongside Airflow.
Have practical experience with GitOps, Config Sync or equivalent tools, and CI/CD for model deployment.
Have strong Python engineering skills, including unit testing and packaging.
Be able to independently own a company-wide platform and build self-service tools.
Be able to produce clear documentation and define measurable SLOs.

Operate and improve secure, reliable ML training and serving infrastructure on GKE and Vertex AI.
Optimize GPU usage, capacity, quotas, and costs.
Build reusable templates, CI/CD pipelines, and self-service tools for independent model deployment by data scientists.
Manage model registry, versioning, deployment, rollback, drift monitoring, and serving health.
Develop workflows using Kubeflow or Vertex AI Pipelines.
Collaborate with data engineers on feature and training data flows across BigQuery and Airflow.
Manage infrastructure as code through GitOps, using Config Sync or equivalent tools.
Enforce least-privilege access to personally identifiable information and data protected by policy tags.
Create practical documentation and define service-level objectives (SLOs) for reliable platform operation and adoption.

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