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Senior role with niche MLOps/GCP skills reduces qualified applicant density.
Highly specialized MLOps and GCP tooling demands limit cross-industry transferability.
Explicit 10+ years total, 6+ years MLOps, and specific GCP/Vertex/Kubeflow/MLflow requirements make filters stringent.
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Design, develop, and maintain end-to-end MLOps pipelines using GCP and related tools (Vertex AI, Airflow, Kubeflow, MLflow).
Build and optimize production ML workflows including data ingestion, transformation, deployment, monitoring, retraining, and CI/CD/CT.
Provide production support including troubleshooting, root cause analysis, continuous improvements, and mentoring team members on MLOps best practices.
Bachelor's degree (full time) required.
Minimum 6+ years in MLOps end-to-end frameworks and 10+ years total professional experience.
Technical expertise in GCP services including Vertex AI, Airflow/Cloud Composer, Kubernetes/Kubeflow, MLflow, TFX, Docker, and proficiency in Python or R programming.
Experience with BigQuery/SQL for data transformation and manipulation.
Experienced in building scalable, production-grade ML pipelines within Cloud environments, specifically GCP.
Strong collaborator comfortable working across Data Science, Engineering, Platform, and Business teams to deliver ML solutions.
Demonstrated ability to lead technical efforts including mentoring and setting MLOps standards and process improvements.