Kubernetes

Kubernetes is used as the core runtime platform for deploying and operating containerized workloads, providing consistent abstractions for scaling, resilience, and service coordination.

It forms the execution layer on which application platforms, data systems, and machine learning workflows are built.

Key Capabilities

• Workload Orchestration
  Supports stateless, stateful, and batch workloads through well-defined primitives.

• Service Networking & Discovery
  Provides stable service endpoints, traffic routing, and policy-based network isolation.

• Scaling & Self-Healing
  Enables automatic scaling, rolling updates, and recovery through declarative health management.

• Extensibility via APIs
  Allows platform-specific behavior through CRDs, operators, and controllers.

• Multi-Tenant Execution Model
  Supports shared clusters with logical isolation and resource governance.

Experience & Platform Contribution

Designed and operated Kubernetes-based platforms to run application services, data pipelines, and ML workloads in production environments.

Key contributions included:

• Defining standard workload patterns for services, batch jobs, and ML inference
• Implementing custom controllers and operators to manage domain-specific lifecycles
• Designing scaling strategies aligned with workload characteristics and demand signals
• Managing resource allocation, scheduling, and isolation for shared clusters
• Establishing secure access and networking boundaries using native Kubernetes controls

Kubernetes acted as the foundational runtime layer, enabling higher-level platforms—such as ML orchestration, GitOps delivery, and observability—to operate in a consistent and scalable manner.