Atif Shafique

Designed and deployed an end-to-end ML pipeline processing 10,000+ video frames daily in production. Trained and optimized deep learning models using TensorFlow, improving inference efficiency by ~25%. Deployed models via AWS SageMaker and Docker, enabling scalable, low-latency inference. Reduced inference latency to <250ms through optimized preprocessing and batch handling. Built analytics dashboards tracking 5,000+ monthly user interactions and transaction metrics. Automated marketing workflows.

Architected workflow orchestration platform automating 20+ business processes in live production. Real-time monitoring pipelines with Google Analytics to track system health Designed modular backend architecture deployed on cloud-hosted infrastructure. Containerized automation services using Docker for scalable and portable deployment. Real-time data pipelines processing operational metrics with low-latency task scheduling. Reduced manual reporting efforts by ~50% through automated dashboards and monitoring

Developed full-stack applications supporting 3,000+ active international users. Designed backend APIs handling concurrent authentication and high-volume transactions. Integrated Sentry for error tracking and Google Analytics for user behavior monitoring. Deployed production services on cloud/hosted environments with optimized database configurations. Improved application response time by ~30% through query optimization and caching. Implemented secure role-based access control across multiple user tiers.

Machine Learning, Deep Learning, Pattern Recognition, Optimization Algorithms, Computer Vision, AI System Deployment

Data Structures & Algorithms, Operating Systems, Computer Networks, Database Systems, Software Engineering