I am an experienced machine learning engineering leader. My experience includes rapid-growth startups and Fortune 500 companies. I have worked directly with founders to build industry changing ML platforms and managed the geographically dispersed teams that deliver the solutions. My true passion lies in developing people. As a leader, I am committed to creating an environment where each team member can grow and thrive - professionally and personally. I invest in the growth and continued development of the people on my team because it leads to a stronger culture yielding better results for the company and our products. I also bridge the gap between my engineering teams and the business leaders with a comprehensive view of the business and the needs of our end users. I have built and led the development teams for enterprise machine learning platforms in highly regulated and global environments. Our products have complex use cases and must serve up to 1M+ end users. I have also owned portfolios of technical products including complex data tools, enterprise platforms, and GenAI solutions that are used by internal and external end users. A few of my wins are: • Delivered $100M+ in value to the enterprise by delivering rules-based product • Saved money and created a pathway to scalable ML development for the enterprise technologies by establishing the frameworks and governance that enable streamlined technology delivery. • Transformed enterprise solutions from a custom coded framework to work with Airflow to enable scale for use by large-volumes in highly regulated use cases. • Piloted a proof of concept successfully to obtain buy-in to investigate the use of JEP to integrate with Akka for greater scalability. • Architected the connection of clusters to ETL for large data volumes from customers using Kafka. • Took ideas through the SDLC including concepting, architecting, and deploying automations of features. • Built aggregators that supported aggregations of 100,000s of features. Tech Stack: Airflow, Spark, PySpark, Sagemaker, XGBoost, PyTorch, Elastic Search, Databricks, AWS, Postgresql, Elixir, GenServer, Phoenix, Python Machine Learning Libraries