Alex Watson

Architected end-to-end ML pipelines on AWS SageMaker and Bedrock, improving high-risk event detection accuracy by 15% and efficiency by 25% while mentoring engineers and reducing deployment errors by 90%.

Developed PyTorch and Scikit-learn models with GenAI enhancements, improving pharmaceutical returns performance by 20% and automating 70% of manual predictive modeling workflows, reducing costs by 20%.

Built full-stack systems and migrated legacy services to AWS, improving uptime to 99.9% and cutting infrastructure costs by 20% while integrating ML features to reduce model bias by 25%.

Engineered full-stack and ML-enabled solutions, migrating systems to AWS to achieve 99.9% uptime and accelerating deployments by 15% while deploying production ML models via Azure ML and MLflow.

Delivered full-stack Django/Flask applications and TensorFlow prototypes, improving client delivery speed by 25% and achieving 90% accuracy on GenAI prototypes while maintaining 99.9% uptime.

Established CI/CD pipelines and container workflows, reducing release cycle time by 40% and improving container management by 25% while implementing real-time APIs with Kafka to cut message latency by 25%.

Enrolled in a PhD program in Information focusing on applied AI/ML research and healthcare applications.

Completed a Master of Applied Data Science with coursework and projects in machine learning, data engineering, and applied AI.

Earned a Bachelor of Science with foundational training in computing and data-related disciplines.