John Joseph

Led high-impact AI initiatives for healthcare and e-commerce clients, developing and deploying ML/GenerativeAI solutions to optimize patient journeys and retail forecasting. Architected scalable backend APIs and secure, HIPAA-compliant ML pipelines on AWS, utilizing LLMOps best practices for model management and continuous improvement.

Implemented real-time ML models using PyTorch and TensorFlow to personalize gaming experiences, enhance matchmaking, and predict churn for top-tier games. Developed recommendation systems, built deep learning models for player behavior, and architected MLOps pipelines using MLflow, Docker, and Kubernetes.

Delivered ML systems for credit scoring, fraud detection, and customer analytics, developing credit risk models and customer segmentation using Scikit-learn and XGBoost. Designed ETL pipelines using Airflow, dbt, and Presto, leveraging GCP for distributed training and deploying models via Google Cloud Run.

Analyzed and optimized global supply chains using ML and predictive modeling, building forecasting models with TensorFlow and Spark MLlib for demand prediction and inventory control. Designed anomaly detection systems and developed dashboards in Power BI/Looker, implementing CI/CD pipelines with Docker and Kubernetes.

Pursued advanced studies in Computer Science. Focused on theoretical and applied aspects of the field.

Completed foundational coursework in Electrical Engineering and Computer Sciences. Gained knowledge in core principles and practical applications.