My Journey
I'm passionate about automation, cloud security, and scalable infrastructure. I've led microservice migrations to EKS, built high-availability Kubernetes clusters with Terraform, and developed a Real-Time DDoS Attack Detection System using AWS and machine learning. As a speaker at LWRedHatDay2023, I've also shared my knowledge on Docker, AWS, and open-source contributions. I thrive on optimizing systems, enhancing security, and driving innovation through automation.
Professional Certifications
Industry-recognized certifications that validate my expertise. Verify on Credly
Check out my latest work
I've worked on a variety of projects, from Kubernetes to AWS to open source contributions. View all projects

Real-Time DDoS Attack Detection System
Initiated and predicted DDoS attacks using AWS-hosted attacker systems, tshark for traffic capture, and a Gradient Boosting Classifier for detection, improving real-time attack detection accuracy by 30%. Published DDoS attack results to a web app via Firebase, improving user awareness and response times during security incidents

Cloud-based Kubernetes Multi-node Cluster Automation
Parallelly initiated EC2 instances master and worker nodes using an optimized asynchronous approach. Incorporated Ansible Dynamic Inventory for efficient gathering of instance public IPs, ensuring automatic connection of all workers with the master. Optimized EC2 instance initiation asynchronously, reducing setup time by 40% and accelerating system startup
Latest Articles
I write about technology, development, and my experiences building products. View all posts
Take a Peek at My Journey
Dive into my professional story and explore my experiences
Work Experience
Skills
I like building things
During my time in university, I attended 1 + hackathons. In two to three days, people from all around the nation would come together and create amazing things. Seeing the limitless potential realized by a bunch of driven and enthusiastic people was eye-opening.
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DDoS Attack Surveillance
Arlington, USA
The project detects Distributed Denial of Service (DDoS) attacks in real-time using machine learning. It classifies network traffic as either malicious (DDoS) or benign by analyzing features from the traffic dataset. This allows the system to block harmful requests early and maintain the availability of services even during high-volume attacks.
Get in Touch
Feel free to reach out through any of these platforms. I am always open to interesting conversations and opportunities.