📍 India | ☁️ Cloud + DevOps + Platform Engineering | 🎓 AIML Student

Building developer tools and applications while learning how to deploy, operate, monitor, and scale real systems.

• Kubernetes Infrastructure & Autoscaling: Building and testing a three-node Kubernetes cluster, provisioning infrastructure with Terraform, and studying workload scaling under load.
• CI/CD Pipeline to Amazon EKS: Automated build and deployment workflow using GitHub, Jenkins, Maven, Docker, Amazon ECR, Argo CD, and Amazon EKS.
• CoderTurtle: AI-powered GitHub pull request review tool. (In progress)
• Developer Portfolio: Personal website and project showcase.
• Designer Portfolio: Design portfolio. (In progress)
• Search Anything You Want: Unified search utility.
• Chat App: Real-time chat application.
• CheckWise: CBC analysis web application using machine learning.
• Scanix AI: Brain tumor detection application.
• GradualBlur: Exponential blur animation package for web interfaces.
• React Bits Contribution: Open-source contribution adding the Gradual Blur animation.

• Building and administering self-managed Kubernetes clusters using kubeadm.
• Provisioning cloud infrastructure with Terraform and working with AWS EC2.
• Deploying containerized applications using Docker and Kubernetes.
• Experimenting with Horizontal Pod Autoscaling (HPA) and resource-based scaling.
• Using k6 to generate load and examine application throughput, latency, and error rates.
• Exploring Prometheus, Grafana, and Kubernetes metrics for monitoring.
• Learning CI/CD pipelines, Linux administration, networking, and infrastructure automation.
• Working with Helm to deploy and manage Kubernetes applications.

• Building developer tools and practical software projects.
• Learning cloud infrastructure, DevOps, and platform engineering.
• Experimenting with Kubernetes deployments, autoscaling, and load testing.
• Exploring distributed systems, networking, and system design.
• Working with Linux, containers, CI/CD, and cloud deployments.
• Designing backend architectures and APIs.
• Building AI-powered applications where they solve a real problem.
• Shipping projects, testing ideas, and learning from what breaks.

• Cloud Architecture and AWS
• Kubernetes and Container Orchestration
• Linux Administration and Networking
• Infrastructure as Code with Terraform
• CI/CD and Deployment Automation
• Monitoring, Observability, and Reliability
• Distributed Systems and System Design
• Load Testing and Performance Analysis
• Backend Scalability

Learn continuously. Build real systems, understand how they work, and make them better.