ENAM HUSSAIN

Designed and deployed a cloud-agnostic corrective control framework across AWS, Azure, and GCP, automating detection and remediation of compliance/security violations and reducing manual audits by 40%. Built Django/Flask/FastAPI backend APIs and scalable microservices to support 20 product teams and improved log search latency by 50%.

Led backend/API development for the Shareworks equity compensation platform, building microservices and Kafka pipelines that processed millions of equity transactions daily with 99.9% uptime and reduced developer onboarding time by 25%.

Developed ML models in PyTorch that reduced fraud false positives by 15% and modernized legacy retirement/insurance systems to Python microservices, cutting data latency by 40% via Kafka/Spark/Airflow pipelines.

Built Python/Django microservices and AWS Lambda functions for retail systems, reducing operational overhead by 30% and improving incident detection with CloudWatch dashboards that cut detection time by 40%.

Developed Django/Flask services and AngularJS frontends for healthcare eligibility, claims, and enrollment workflows, created Kafka/Spark ETL pipelines that halved batch job times and reduced support tickets by 25%.

Completed a Master of Science in Computer Science, focusing on scalable architectures, AI/ML integration, and cloud-native services.

Completed a Bachelor of Science in Computer Science with coursework and projects in software engineering, distributed systems, and databases.