Hi, I'm
Building scalable Kubernetes platforms and ML infrastructure. MS Computer Science @ NC State University.
Software developer with a passion for cloud and platform engineering. I automate obsessively — release pipelines, build promotion, change management, the full lifecycle of getting code safely from a branch to production.
Lately deep in Kubernetes: staring at CrashLoopBackOffs, writing Helm charts, configuring Ingress controllers, untangling cluster networking. Pursuing my Master of CS at NC State University
Liquid Rocketry Lab — Raleigh, NC
I'm building a Kubernetes-based distributed replay platform from scratch — owning everything from cluster topology and network architecture to deployment automation. One of the more interesting problems has been enabling 1:1 user-to-pod isolation through carefully crafted Services and Ingress routing, so every concurrent session gets its own sandboxed environment. I automated the full environment provisioning lifecycle with Jenkins, so new replay instances spin up and tear down without any manual intervention.
The platform now handles up to 20,000 concurrent replay sessions with dynamic resource orchestration, and has held 99.999% availability under sustained simulation workloads across both Akamai and AWS EKS clusters.
Brane Services Pvt Ltd — Bengaluru, India
My main focus was making 15+ microservices more reliable and cost-efficient. I built an internal reliability platform that automated capacity benchmarking and surfaced performance trends before they turned into production incidents. On the cost side, I cut AWS compute spend by 25% — mostly through instance right-sizing and shifting 60% of workloads to spot instances with smart scheduling policies.
I also built a load testing framework with K6 wired directly into the Jenkins CI/CD pipelines as a performance quality gate — if a build failed load tests in staging, it didn't go to production, full stop. That alone cut manual testing effort by 40% and kept regressions from slipping through. To close the loop, I set up a Grafana and Prometheus observability stack that gave the whole team real-time health visibility across every environment.
End-to-end MLOps pipeline for training, versioning and deploying a churn prediction model. GitOps deployment with Argo CD, automated CI with GitHub Actions, and scalable inference with KServe on Kubernetes backed by AWS S3.
Robust CI/CD pipeline for a Node.js app with shift-left security — pre-commit hooks for static analysis, CI scans for vulnerabilities, Ansible playbooks for config management, and a k6 performance quality gate blocking regressions in staging.
North Carolina State University
Raleigh, NC, USA
Coursework: Algorithm Design, DevOps, Computer Networks, Cloud Computing, Software Engineering
Vidyavardhaka College of Engineering
Mysuru, India
Awarded to attend AWS re:Invent Las Vegas 2025 for contributions to Cloud and DevOps learning.
Associate-level certification in designing distributed systems on AWS.
Completed Architecting with Google Compute Engine specialization on GCP.
TA for IT Concentration Business Management. Active in Google Developer Student Club Cloud Chapter.
Open to full-time roles, internships, and interesting projects.