Designing resilient cloud platforms that ship faster and fail safer

Problem: I wanted to move into Cloud/DevOps, but my experience and knowledge were scattered across IT, self-study, and side projects.

Approach: Built a structured learning path around Linux, networking, containers, and scripting, with daily hands-on labs instead of just videos.

Outcome: Strong fundamentals in Linux, the OSI model, and shell tooling—enough to debug real systems, not just pass quizzes.

Problem: I needed more than "how to click in the console"—I wanted to understand how to design secure, scalable systems end-to-end.

Approach: Went deep into Google Cloud architecture: IAM, networking, storage, and resilience patterns. Worked through case studies and built small reference architectures.

Outcome: Passed the Google Professional Cloud Architect exam and gained confidence designing multi-tier, secure cloud solutions.

Problem: I wanted a place to experiment with "real" infrastructure—Kubernetes, GitOps, observability—without running up a massive cloud bill.

Approach: Built a Proxmox-backed homelab running a multi-node k3s cluster, wired up GitOps with ArgoCD, storage, ingress, and monitoring.

Outcome: Production-like environment where I run real services, practice upgrades, troubleshoot failures, and treat infra as code.

Problem: I needed to demonstrate end-to-end ownership—from design and development to deployment and operations—across diverse technical stacks.

Approach: Built and deployed production applications including an AI-powered knowledge management system, serverless lead capture with AWS Lambda/DynamoDB, and a task management platform—each showcasing different cloud-native patterns.

Outcome: Portfolio of working applications that demonstrate my ability to design, build, deploy, and operate real products end-to-end across multiple cloud platforms.