Contributed to a data pipeline ingesting and processing 10M+ records for targeted marketing, improving segmentation and reporting capabilities.​

Refactored critical modules to boost throughput, cutting server load and API latency for high‑volume campaigns.​

Integrated analytics and automation features that improved campaign targeting accuracy by 13–15% during initial rollouts.​

Led a cross‑functional team of 4–6 engineers to refactor a legacy Node.js system into Dockerized services, reducing unplanned downtime by ~28% and speeding up releases through CI/CD automation.​

Partnered with product and design to launch 10+ user‑facing features that increased engagement and retention across the platform.

Diagnosed and fixed production issues, reducing critical bugs by ~40% and improving page reliability and load times.​

Automated content workflows with Cron Jobs, cutting manual updates by ~30% and streamlining operations for the web team.

Designed and deployed advanced machine learning models, including Convolutional Neural Networks (CNNs), achieving 96% accuracy in early detection of hematological and oncological abnormalities in medical imaging.

Researched and enhanced Multi-Path TCP (MPTCP) protocols, increasing data transfer efficiency by 42% across heterogeneous networks through simulations on CloudLab and FABRIC testbeds.

Collaborated on large-scale network experiments, resolving kernel compatibility issues and improving real-world MPTCP performance by 27%, enabling seamless data transmission in diverse network environments.