Jobs → Engineering → Senior MLOps Engineer (GCP)

About the Role We are seeking a Senior MLOps Engineer to architect and build our next-generation, end-to-end ML Platform-as-a-Product. This role focuses on designing a unified framework that abstracts infrastructure complexities, enabling ML Engineers and Data Scientists to seamlessly write, train, deploy, and monitor models in production. Key Responsibilities • Standardize notebook environments, local CLI tools, and SDKs to make model writing highly repeatable and containerized from day one. • Build automated, scalable training pipelines utilizing Vertex AI Pipelines, Kubeflow, and Databricks, integrating unified data access layers. • Construct robust CI/CD/CT (Continuous Training) pipelines that package models into auto-scaling microservices on Google Kubernetes Engine (GKE) or Vertex AI Endpoints. • Implement end-to-end observability frameworks to track system performance, monitor data/concept drift, and trigger automated alert-and-retrain loops. • Manage the enterprise Feature Store, artifact registries, and unified metadata tracking (lineage) for compliance and governance. Requirements • Proven experience building a unified "ML Platform-as-a-Service" inner-source framework for internal engineering consumption. • Deep expertise in Vertex AI (Pipelines, Feature Store, Model Registry, Endpoints), GKE, BigQuery, and Google Cloud Storage. • Strong hands-on experience using Terraform to provision secure, compliant, and cost-optimized ML infrastructure. • Experience setting up end-to-end automation covering ML GitOps, automated testing, and zero-downtime canary deployments.