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Mid-level, metro location, and recognizable brand increase applicant density despite MLOps specialization.
Role requires specialized MLOps and GCP expertise, making cross-industry transferability limited.
Explicit 5-8 years plus mandatory GCP, Vertex AI, Terraform, and CI/CD skills raise 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.
Manage end-to-end lifecycle of ML models including deployment, monitoring, retraining, rollback, and governance in production environments.
Implement CI/CD pipelines and Infrastructure-as-Code (Terraform) for repeatable provisioning and environment management of ML applications.
5 - 8 years of experience in Cloud Engineering, MLOps, or ML Platform Engineering.
Hands-on expertise with Google Cloud Platform (GCP), Vertex AI, and production-grade ML deployment architectures.
Proven experience deploying and operationalizing Python-based machine learning models.
Bachelor’s degree in Computer Science, Engineering, Information Technology, or related discipline.
Experienced in designing scalable, reliable, and secure ML deployment pipelines and infrastructure on GCP.
Strong operational focus on production support, monitoring, and lifecycle governance of ML services.
Proficient in automation of ML workflows using CI/CD tools, Infrastructure-as-Code, and cloud-native services for enterprise-scale ML environments.