Raymond Allen

Developed high-performance machine learning and optimization libraries in Rust, achieving faster production performance than existing implementations. Designed and deployed a Bayesian hyperparameter optimization framework, taking it from initial specification to production deployment. Built a deep learning computer vision model for UI element detection using PyTorch and AWS, enabling automated interface parsing and visual analysis.

Designed and deployed a cloud-based automated cryptocurrency trading system with end-to-end ML automation. Built custom time-series forecasting models for financial data using deep learning and classical machine learning methods. Integrated LLM-based news and social-media sentiment analysis to enhance predictive signal generation. Implemented a full MLOps framework enabling continuous data acquisition, automated retraining, and seamless redeployment of updated models, supported by infrastructure-as-code on AWS. Conducted extensive backtesting, A/B testing, and hyperparameter optimization to maximize model robustness.