At ChargePoint, I contributed to backend engineering projects that improved the security, reliability, and visibility of our EV charging platform. I built a FedRAMP-compliant authentication and authorization service leveraging ChargePoint’s SSO to generate and issue JWTs, securing access for 300,000+ charging stations. I containerized the Django service with Docker and deployed it on a Kubernetes cluster to ensure it ran reliably at scale.

I also enhanced observability by instrumenting applications with Prometheus, building Grafana dashboards, and integrating Sentry for error tracking and alerts, giving our engineers real-time insight into production. On the development side, I built off of the CI/CD pipeline with pre-commit hooks and custom GitHub Actions to automate code coverage and tooling, helping enforce best practices and maintainability across the team.

At BIT Lab, I applied a wide variety of machine learning and statistical techniques to analyze large-scale datasets and uncover meaningful patterns in the data. Some of my work includes, but is not limited to, autoencoders, unsupervised learning methods, topic modeling, and conducting Differences-in-Differences (DiD) analyses to support causal inference.

I also developed a multithreaded data collection pipeline that used rotating proxies and authenticated API access to efficiently gather large volumes of user activity data. The collected data was structured and stored in a MySQL database.

At ChargePoint, my role as a Integration Engineer Intern focused on designing and developing secure APIs using MuleSoft to connect critical business systems, including Salesforce, NetSuite, and ChargePoint’s proprietary customer-facing platforms. I defined API specifications in RAML and leveraged DataWeave for data transformation, ensuring high-quality, optimized integration processes.

In addition, I developed robotic process automation solutions with UiPath to streamline complex tasks and improve operational efficiency.