- Developed CI/CD pipeline and build system improvements for commercial DLP Endpoint software- Managed packaging and deployment of Chrome, Firefox, and Safari extensions associated with the endpoint products along with JAMF administrative deployment procedures- Personally maintained build infrastructure for both the Windows and Mac DLP development teams- Delivered Mac endpoint builds that ran natively on both Apple M1 and Intel x86-based systems- Rewrote Jenkins CI/CD pipelines for Mac and Windows DLP endpoint products from scratch and transitioned them to cloud-based Jenkins CI/CD deployment- Replaced legacy build systems of the Mac and Windows products with new CMake-based build systems - Integrated static analysis and code coverage tools CI/CD pipelines to provide security vulnerability and code coverage information- Automated license and security vulnerability audits in third-party code- Brought a web content and text analytics engine to commercialization, rearchitecting an existing HTML scanner and creating both an SELinux-based file containment system as well as an asynchronous C++ web server frontend with push notifications- Co-developed a prototype cross-platform endpoint software product, creating a build and packaging system using CMake and CPack to work on all supported platforms as well as a telemetry module that used Facebook’s OSQuery to gather user login and system state information- Prototyped a malware sandboxing and reporting system, creating a Django-bansed web server, RabbitMQ task delegation system for sandboxing jobs, a Redis database backend, Docker-based deployment and basic Amazon EC2 deployment with load balancing- Developed a Python-based web server and Grafana analytics portal over 4 weeks for a data ingestion / data lake system used by malware researchers

- Personally designed, developed, and maintained Python automation for the following tasks:• Identifying known security vulnerabilities across multiple chipset product lifecycles and blocking the release of affected software, ensuring that over 30 software releases a day aren’t shipped to customers without necessary fixes• Identifying and grouping together reports for different types of Linux kernel and Android crashes based on pattern recognition, eliminating over an hour of time spent per team member per day spent doing the same work manually• Tracking software change propagation across internal software releases to determine if and when known crashes have resurfaced after supposedly being fixed, leading to 50 unresolved crashes being re-identified over a period of 6 months• Scanning and cataloguing of version information for third-party software distributed alongside Android builds, allowing security audit teams to easily identify and address vulnerable versions- Re-architected existing Python JIRA automation used to extract and triage reports for Linux kernel and Android crashes on internal chipset hardware, improving processing speed by 600% and allowing it to handle over 30,000 crash reports per month- Managed distributed computing clusters and SQL databases for all team-specific automation

- Created and deployed a modular crash analysis utility able to parse phone hardware memory dumps and extract crash information using various Perl and Python scripts maintained by other Qualcomm debugging teams- Linux Kernel driver-level crash analysis in C and ARMv7/ARMv8 assembly as part of commercialization efforts for four separate flagship Qualcomm Snapdragon chipsets- Debugging of commercial SoC chipsets via third-party (Lauterbach) Trace32 JTAG debugging hardware with Linux integration- Assembly-level debugging of hardware-induced RAM/cache issues, CPU hardware errata, and defective chipset samples returned from chipset customers- Onsite software debugging support for chipset customers