Google Cloud

Google Cloud Platform (GCP) is used as a primary production environment for running application, data, and machine learning workloads, with particular strength in analytics, Kubernetes-native services, and managed ML capabilities.

It is approached as a product-centric cloud platform, optimized for data-intensive systems and ML-driven applications.

Key Capabilities

• Compute & Kubernetes Runtime
  Supports containerized and VM-based workloads using Google Kubernetes Engine (GKE) and Compute Engine.

• Data & Analytics Stack
  Provides scalable analytics and storage through Cloud Storage and BigQuery for large-scale data processing.

• Managed ML & AI Services (Vertex AI)
  Enables training, evaluation, deployment, and lifecycle management of ML models using a unified platform.

• Platform-Native Integration
  Strong interoperability between compute, data, and ML services simplifies system design and operations.

• Operational Simplicity
  Emphasizes managed services that reduce infrastructure overhead while maintaining reliability.

Experience & Platform Contribution

Designed, deployed, and operated production workloads primarily on GCP, spanning application services, analytics pipelines, and machine learning systems.

Key contributions included:

• Running production ML applications using GCP-native services
• Leveraging BigQuery for large-scale analytics and downstream data consumption
• Operating Kubernetes-based microservices on GKE within shared platform environments
• Using Vertex AI for managed ML workflows, including training and deployment
• Advising teams on architectural decisions aligned with GCP’s strengths in data and ML

GCP served as the main execution environment for products, enabling teams to build data- and ML-driven systems with a strong balance of scalability, reliability, and operational efficiency.