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Mid-level metro MLOps role with common GCP skills but some Vertex AI specialization.
Specialized GCP and Vertex AI MLOps expertise reduces transferability across unrelated industries.
Explicit 5–8 years requirement plus mandatory GCP, Vertex AI, Terraform and CI/CD skills increases filtering strictness.
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Design, build, and maintain scalable MLOps frameworks and automated deployment pipelines for Python-based ML models on Google Cloud Platform.
Implement and manage CI/CD pipelines, Infrastructure as Code (Terraform), and cloud-native ML infrastructure to ensure reliable, scalable, and secure production ML services.
Monitor deployed ML models and infrastructure for performance, operational health, and compliance; troubleshoot and optimize platform stability and cost efficiency.
Bachelor's degree in Computer Science, Engineering, IT, or related field.
5-8 years of experience in Cloud Engineering, MLOps, or ML Platform Engineering.
Strong hands-on experience with Google Cloud Platform, specifically Vertex AI, Python-based ML model deployment, CI/CD automation, and Infrastructure-as-Code (Terraform).
Work Experience Required: 5 - 8 years in relevant roles.
Experienced in operationalizing production-grade machine learning solutions on GCP with emphasis on automation, monitoring, and reliability.
Comfortable managing end-to-end ML lifecycle including deployment, versioning, retraining, and rollback within enterprise-scale environments.
Skilled in building integrated pipelines combining source control, testing, and deployment with infrastructure automation for efficient ML operations.