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Recognizable multinational, popular ML/MLOps title, mid-level experience, and likely metro hiring increase competition.
MLOps and GCP-specific skills are transferable across industries but require cloud-specific experience.
Explicit 6+ years and mandatory GCP/Vertex/Airflow/Python requirements increase filter strictness.
Build and support end-to-end MLOps pipelines on GCP using Vertex AI, Airflow/Kubeflow, ensuring scalability and sustainability of ML/AI model deployments.
Manage the ML model lifecycle including feature engineering, deployment, monitoring, retraining, performance optimization, and operational support.
Collaborate cross-functionally with Data Science, Data Engineering, Cloud Platform, and MLOps teams while following best practices, coding standards, and documenting technical work.
Bachelor’s degree (full time).
6+ years of analytical experience with at least 3+ years in AI and Machine Learning.
Expertise in BigQuery/SQL, Vertex AI, GCP services, Python for ML pipelines, and Airflow/Cloud Composer/Kubeflow experience.
Work Experience Required: 6+ years, including 3+ years in AI/ML.
Experienced in operationalizing ML models with strong knowledge of Google Cloud Platform and MLOps tools and frameworks.
Demonstrates capability in managing full ML lifecycle including CI/CD/CT, monitoring, and containerization for production deployment.
Able to collaborate effectively across diverse technical teams and communicate progress and risks clearly.