Developed macOS applications using Swift and SwiftUI to control serial devices, including spectrometers, for scientific and hardware-driven workflows.• Engineered scientifically accurate spectral processing scripts in Python and Swift, leveraging spectral math to analyze and process light data from 25 spectrometers in parallel efficiently.• Implemented advanced spectral graphs and real-time voltage plotting from Arduino, enabling precise visualization of sensor data.• Built automated systems that trigger scripts based on photodiode signals, streamlining measurement workflows and ensuring reliable real-time hardware performance.

Development of Artificial Intelligence SAAS Platform for Automated School Grading.• Secured $150,000 in credits from Microsoft and $350,000 from Nvidia to support the launch of ExamAi.• Developed the AI SAAS platform end-to-end, resulting in $1.6M in seed funding from investors.• Led the technology team, overseeing 6 engineers and 4 designers.• Created a two-year growth strategy and assigned its execution to an 8-person business team.• Built tools to support expansion, including an AI-driven system generating 10,000 automated emails and a comprehensive salary database for U.S. colleges, and automated data visualization dashboards.

Leadership over Software Engineering Support team part of Meta Reality Labs.• Software & Hardware Validation using Python and full stack development of Internal Equipment Control mobile app using Flutter, Dart, Swift and Python. • Managed Reality Labs internal Networks and established a 3D printing lab, saving $300k+ annually with CAD Rapid Prototyping, 3D scanning, and diverse 3D printing techniques (FDM, SLA, MJF).• Automation of AR/VR headsets, Android devices (using ADB), Linux DevServers, and Network Hosts.• Implemented Camera Imaging Simulators in several Multi-GPU Cloud services such as GitHub Actions with AWS EC2 Containers to reduce costs from $1,051,200 to estimated $14,600 per team per year.• Research of Camera frustums and Fields of view by creating a 3D automation workflow using Blender Python and CUDA (Nvidia GPUs) for Optical Camera Imaging simulations to create 1M+ GLTF scenes.

IOT Automation Engineer Consultant at Signify Phillips and Halliburton• Created automation framework for engineers to test Front-End and Back-End using process parallelization with Python, Selenium, Robot Framework, and Mosquitto API (MQTT). • Front-End Automation by web scraping to test web elements in Halliburton Frac web application.• Full-Stack Hardware/Software Automation testing of Wireless Area Connectors made by Signify Phillips.

Research of Convolutional Neural Networks used for classifying images at large scale.• Wrote production-level Python code, trained, and fine-tuned Deep Learning Neural Networks (mostly CNNs) with Machine Learning frameworks such as TensorFlow, Numpy, CUDA, and OpenCV. Model GPU computing using multiple Nvidia graphics cards.• File management scripting with Python in UNIX/Linux environment via ssh connection to build and organize neural networks on several datasets such as ImageNet, CIFAR10, MNIST, COCO2017, etc.• Recreated TensorFlow from scratch to build a proprietary library faster and more efficient, implementing Keras with newer versions of famous models like VGG16, Resnet50, Inception and DenseNet.

Quality Assurance and Software Automation of Avionics Hardware equipment.• C and Python code review and documentation of test requirements. Assisted with test/validation, software identification, Code Coverage, and requirements checks.• Developed code verification by analysis procedures for a DO-178 B Level C project, using Python, C, C++, Shell, and Bash and interacting with hardware for tests. • Script automation with Python and Bash to run multiple programs sequentially as well as file management automation.

Python instructor through the University of New Mexico at Washington and Polk School in Spanish and English to bilingual students.• Coursework taught: Python, Object Oriented, Functions, Libraries, Pyplot, etc.