Led a 6-month effort to update the codebase from Python 2 to Python 3 and improved test coverage and coding standards along the way

Developed and enhanced the product by writing and debugging Python and JavaScript code for malware analysis tasks, web backend code, and web frontend code; increased detection rates of malicious patterns, performed analyses quicker, and distilled verbose results into actionable data

Coordinated weekly deployments to production; ensured recently-added features functioned as expected and no issues appeared in production after deployment

Created Python unit tests and Selenium-based UI tests to ensure code correctness and help prevent future bugs

Constructively participated in code reviews and used Agile methodologies for working with eight other engineers to improve and maintain the product

Leveraged state-of-the-art academic research on machine learning security to implement evasion and model-stealing attack algorithms in Python to be used against deep neural network models

Streamlined the process for integrating new attack algorithms by using Docker containers to compartmentalize services and designed a Protobuf-based protocol for standardizing communication between master and worker containers

Used React to build a web-based user interface that communicates with a Python-based web backend server over a RESTful API.

Trained and tested machine learning models using TensorFlow 2.0 and Keras, to validate attack algorithms.

Designed and implemented an abstraction layer in Python over the SciDB database system for storing and retrieving multidimensional datasets.