• Created and led product roadmap for the first iteration of machine learning platform to streamline the development and deployment process of Scikit-learn and TensorFlow models• Designed the platform architecture using Airflow, MLflow, Snowflake, and Domino. Shortened the deployment time by 80% • Led the product direction for a team of 6 engineers and presented insights to management and key stakeholders• Worked cross-functionally with 20 data scientists to manage portfolio of 50+ models• Planned and implemented migration of platform from self-managed EC2 instances to utilize Amazon SageMaker for batch and real-time deployment of models

• Developed an API for conversation intelligence platform using GraphQL, Kotlin and Spring Boot• Containerized models using Docker and deployed to AWS

• Developed a model forecasting customer satisfaction• Built a high quality and trusted data source leveraging custom SQL queries• Tuned and evaluated the performance of several machine learning algorithms• Automated periodic updates to model output using Docker and Airflow

• Modernized existing legacy code to leverage Jakarta EE 8 and Microprofile API• Decoupled monolithic behavior into microservices for deployment onto OpenShift 3.11• Assisted with development of CI/CD pipelines, using Jenkins, that reduced time from code check-in to application deployment from several hours to a few minutes