• Engineered scalable backend systems using Redis, Kubernetes, and Python microservices, ensuring platform reliability during high-demand events like the 2024 Super Bowl with 3x traffic surges.

• Implemented distributed traffic management leveraging Redis and Python, mitigating service overload, reducing request delays by 30% during unexpected peak loads.

• Optimized real-time chat infrastructure by introducing rate-limiting with Redis and Elixir, enhancing readability in live streams and boosting user engagement by 20%.

• Revamped notification delivery platform using RabbitMQ, Python, and asynchronous processing, improving alert delivery speed by 40% for millions of users.

• Designed and deployed Whatamix feed platform utilizing DAGs and modular architecture, written in Python, enhancing content personalization and boosting discoverability by 25%.

• Conducted rigorous load testing and addressed system bottlenecks with Python, Kubernetes, and performance analytics, enabling the platform to scale for 5x traffic.

• Optimized database and GraphQL query performance through async I/O and resolver enhancements, reducing latency by 40% in critical workflows.

• Collaborated across teams to adopt Python-based solutions for distributed systems, fostering a cooperative environment and enhancing cross-team workflows for scalability.

• Contributed to scalable backend systems for e-commerce logistics and inventory, leveraging Python, Kubernetes, and GCP, enhancing operational efficiency and customer satisfaction across millions of users.

• Optimized last-mile delivery operations by developing a FastAPI-based machine learning model with Docker and Uvicorn, reducing delivery delays and saving $4–5M annually.

• Enhanced inventory reservations API scalability using actor pattern, Kubernetes, and CosmosDB, increasing throughput by 150% during peak traffic, processing 5,000 orders per second.

• Rebuilt Walmart’s autocomplete backend by implementing prefix hash trees and Memcached in Kubernetes, achieving O(1) query time and reducing response latency by 20%.

• Improved partner integrations by building Spark-based pipelines in GCP, enabling secure API feeds via signed URLs, reducing data processing time by 75%.

• Scaled real-time data pipelines for A/B testing with Kafka, Spark, and M3DB, handling 250,000 messages/second, improving experiment metric processing to under three minutes.

• Collaborated effectively with cross-functional teams to troubleshoot issues, ensure operational excellence, and maintain high-quality deliverables under tight deadlines in high-pressure environments.

• Completed Flask, Python based REST API development and document generation wrapper classes for the existing JRPC system for module-resource identification.

• Leveraged React, Redux, Material UI, and Figma to create dynamic UI features, enabling personalized product designs, increasing user satisfaction by 15%.

• Drove the development of the Lifetouch mobile app using React Native, Node.js, MongoDB, and GraphQL, with exploration of potential AI-powered recommendation features.

• Created AWS Resource Management Dashboards to monitor and manage AWS resources effectively by predicting future costs and redundant resources decreasing the average metric usage by 25% for DB and SQS resources.

• Majorly maintained and improved Learning A-Z product suite, focusing on building user interfaces using AngularJS and later adopting React.

• Migrated parts of a legacy web application to a more modern React codebase, improving performance and SEO.

• Implemented dynamic website navigation, improved performance, and fixed various bugs to enhance accessibility and overall user experience.

• Followed an agile SDLC process with daily stand-ups, sprint planning, and retrospectives.

• Conducted unit testing using tools like Jest to ensure code quality before deployment.