Syed Muhammad Zafar Alam
I am a passionate computer engineering student at the University of Waterloo, specializing in software engineering and artificial intelligence.
About
I am a passionate computer engineering student at the University of Waterloo, specializing in software engineering and artificial intelligence. Over the past several years, I have gained hands-on experience building scalable applications, developing AI-powered pipelines, and optimizing systems for real-time performance. I enjoy translating complex problems into elegant solutions using cutting-edge technologies such as AWS, React, and Python frameworks.
Throughout my internships and projects, I’ve developed a strong focus on full-stack development, cloud-native architectures, and machine learning productionization. I am driven by a desire to make impactful contributions to technology that enhance user experiences and operational efficiencies.
- 3 years' experience
- Joined August 2025
Industry experience
- Healthcare
- Software & Internet
- Government
- Financial Services
- Gaming
Work experience· 4 roles
Engineered a GPT-powered parser to extract structured data from medical documents, cutting manual review time by 50% for clinicians. Developed a database-backed distributed task queue reducing API latency by 25% by offloading background jobs. Architected asynchronous pipelines to ingest and validate wearable device data, storing metrics in PostgreSQL which enabled sub-200ms updates for real-time health dashboards.
Rebuilt customer-facing site using React and TypeScript, improving SEO score from 56 to 92 and boosting traffic by 20%. Designed multi-tenant SaaS platform architecture on AWS EKS enabling isolated pods with role-based permissions and frontend integration. Optimized a CNN-autoencoder ensemble for anomaly detection, improving accuracy to 91% and F1 score to 89%. Productionized ML pipeline on AWS Lambda with IAM-secured roles for low-latency SaaS predictions.
Automated reporting workflows in proprietary database system, reducing report generation time from 24 hours to 2 hours. Developed mappings from external document structures to database relationships, improving integration time by 90%. Reorganized database tables and relationships enabling faster access and insights into related information.
Education
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