Graduated with a Diploma in Shipbuilding Technology. Spent three years in an environment where precision isn't a preference — it's a structural requirement. My first real automation work happened on a shipyard floor, not a server rack: streamlining CAD/CAM workflows and documenting complex manufacturing systems for teams that couldn't afford ambiguity.

While still working in engineering, I taught myself Python and Shell scripting at nights and weekends. Built internal tools that cut documentation processing time by 40%. That number got attention — and it confirmed that automation could be my full-time work, not just a side project. I left engineering behind and didn't look back.

Made the full transition into DevOps. Enrolled in the DevOps Career Track Season 2 by Poridhi for structured, hands-on depth alongside real project work. Previously drove enterprise-level cloud migration projects achieving 60% faster deployment cycles. Every project on this GitHub reflects real implementation — not tutorials, not templates, not copy-pasted YAML.

Pursuing a Bachelor's in Computer Science (Cloud Computing) while preparing for the AWS Solutions Architect Associate exam. My learning style is hands-on first: I build the thing, break it, rebuild it better, then write about it. That's what the repos and blog posts are — documented evidence of the process.

Enterprise-grade Kubernetes infrastructure on AWS EKS, designed for multi-environment production deployments. Implements a complete IaC stack covering cluster provisioning, node group management, IAM role binding, and network policies — built to be cost-effective without sacrificing scalability or reliability.

Production-ready K3s lightweight Kubernetes cluster on AWS, fully automated through Terraform. A complete infrastructure-as-code blueprint covering EC2 provisioning, VPC networking, and K3s cluster bootstrapping — demonstrating that lightweight doesn't mean cutting corners on production readiness.

Hands-on implementation of Kubernetes Horizontal Pod Autoscaler — the mechanism that separates truly elastic systems from ones that just claim to be. Builds a real workload, fires synthetic traffic at it, and watches the cluster respond: pods scale out under load, scale back when it clears. Includes metrics-server setup and threshold tuning.

Implementation of zero-downtime deployment strategies using Kubernetes rolling updates and rollbacks. Covers maxSurge/maxUnavailable tuning, readiness probe configuration, and rollback execution — the real ops work that keeps services alive through deployments. Includes failure simulation and controlled recovery.

Comprehensive Jenkins CI/CD implementation from bare infrastructure to a fully operational distributed build environment. Covers automated server provisioning on AWS, controller-agent architecture configuration, job management, and enterprise-grade automation practices — the full pipeline lifecycle, not just a hello-world pipeline.

A multi-container system where NGINX doesn't just run — it's watched. A dedicated watcher container continuously polls the server's health; on failure, a mailer container fires an automated alert instantly. A clean, composable implementation of the sidecar monitoring pattern that reflects how production observability actually works at the container level.

Storage in Kubernetes is where stateless container thinking collides with stateful reality. This article breaks down PV/PVC architecture, storage classes, and access modes — with production-ready patterns for engineers managing data-bearing workloads.

Kubernetes Secrets are powerful and frequently misunderstood. This guide covers Secret types, creation strategies, RBAC scoping, and encryption at rest — everything you need to handle credentials, tokens, and keys safely in a production cluster.