• Joined as an early engineer (~3-person team) at an AI2 Incubator-backed B2B SaaS, owning end-to-end feature delivery across the full stack

• Built multi-tenant auth system (signup, login, email verification, Google SSO via Cognito) backed by DynamoDB + JWT

• Shipped Shopify sync worker (Python) that auto-creates draft products from approved orders with full idempotency

• Built PDF/Excel line item extractor: Gmail → Google Drive → OpenAI → structured JSON for automated purchase order ingestion

• Maintained Vercel CI/CD pipeline across 50+ deploys with zero downtime

• Backend Developer with hands-on experience in maintaining and optimizing backend service systems

• Strong focus on digital accessibility, including WCAG 2.1 and Section 508 compliance

• Currently working with the University of Illinois Digital Accessibility Remediation Team

• Ensuring backend applications are reliable, scalable, and inclusive for all users

• Faced high infrastructure costs; helped containerize apps with Docker and deploy to AWS ECS Fargate, reducing manual server management by ~25%.

• Deployment caused occasional downtime; supported Blue-Green Deployment setup in AWS, resulting in nearly 100% uptime during releases.

• Traffic spikes led to inconsistent performance; created basic Python scripts for auto-scaling, helping maintain system responsiveness during peak hours.

• Manual deployments were slow; contributed to Jenkins-based CI/CD pipelines, reducing deployment time by ~40%.

Participated and executed several leadership programs

Helped execute several Scholarship and STEM Programs

Community Engagement of Shooting Stars Stakeholders

• Scaling microservices was inefficient; assisted in orchestrating them using Kubernetes on ECS, improving scalability and reducing error rates.

• Alerting was delayed; helped configure AWS CloudWatch and Slack integration for real-time alerts, cutting incident response time by 50%.

• Resource provisioning lacked consistency; used Terraform to automate EC2, S3, and VPC setup, ensuring repeatable and error-free deployments.

• Search performance was lagging; supported tuning Elasticsearch on AWS to handle large query volumes, improving query speed and stability.