MLOps Engineer

Build automated ML pipelines for training, validation, deployment, and monitoring using Vertex AI Pipelines, Kubeflow, or TFX

• Leverage Vertex AI Workbench, Training, and Experiments for reproducible, collaborative model development
• Deploy, manage, and monitor models using Vertex AI Model Registry, Model Monitoring, and Prediction Endpoints
• Collaborate with Data Scientists to productionize notebooks and prototypes into scalable ML services
• Monitor model performance, detect data/model drift, and ensure data quality using Vertex AI Monitoring
• Containerize and orchestrate ML workloads using Docker and Kubernetes (GKE)
• Build and maintain robust CI/CD pipelines using Cloud Build, Jenkins, or Bitbucket Pipelines
• Ensure strong version control, security, and compliance across the ML lifecycle
• Maintain comprehensive documentation, templates, and artifacts to enable reproducibility, governance, and fast onboarding