+ Engineered a comprehensive software management system for Starlink satellite-enabled hardware platforms, overcoming connectivity challenges in remote areas.+ Developed a unified, remote-accessible user interface for complex and expensive custom electronics, streamlining control and enhancing usability.+ Designed and implemented a scalable master-slave distributed computing system, optimizing the computational load of machine learning tasks with real-time data visualization.+ Architected and deployed a Kubernetes-based application with containerized microservices using gRPC for inter-service communication, enhancing system robustness and scalability.+ Created sophisticated workflows for hardware interaction via custom hardware APIs, maintaining state management and integrating custom command sets.+ Led the backend development and constructed all gRPC connectors for seamless frontend integration, ensuring efficient and reliable system performance.+ Executed direct memory access operations to boost system efficiency and reliability, demonstrating advanced technical proficiency in low-level system optimization.

+ Developed a Retrieval-Augmented Generation (RAG) system with a vector database backend, enabling conversational interaction with data. Designed workflows to store embeddings, perform efficient similarity searches, and retrieve contextually relevant information for real-time question answering and decision-making.+ Created an innovative entity-level sentiment model (combining two NLP tasks into one model) allowing for varying sentiments to be applied to different entities within the same sentence, based on context-specific sentiment analysis.+ Optimized machine learning workflows by rewriting critical Python pre and post-processing components in Rust, achieving significant performance improvements. This refactoring reduced latency and increased throughput in data handling tasks, effectively optimizing system efficiency for real-time analytics applications.+ Developed a system to prevent duplicate content from being processed by identifying and comparing similar texts. Used a database to check for matches and deployed the solution in a modern cloud-based environment, ensuring the system runs smoothly and efficiently.+ Designed and implemented a scalable SaaS platform that processes 60 million documents daily, leveraging a Kubernetes-based multilingual machine learning framework capable of analyzing text in 90 languages. Developed custom models for language identification, intelligent routing, and real-time topic classification, presenting customer-relevant insights on topics and corporate reputation through an intuitive dashboard. This system enables businesses to monitor emerging trends and maintain brand awareness at scale.

+ Collaborated with L3Harris to develop a neural network driven application in a sprint for a government project that extracted and identified audio domain artifacts. Packaged the application using OCI standards and deployed it as an API-wrapped microservice on a Kubernetes cluster for seamless access and scalability.+ Spearheaded the design and development of a user-capability driven application, featuring a drag and drop interface, that empowers users without machine learning expertise to train and implement their own neural networks using raw, labeled data. The application facilitates automatic labeling of datasets for training, streamlining the process of model development and allowing customers to take charge of their own machine learning solutions.+ Developed a computer vision application valued at $1.4 million that identified and scanned visual artifacts quickly with high accuracy, beating out a slower, less accurate competitor application. The 13-week project utilized image morphology, cv-transformers, and feature mapping and was delivered as both a monolithic application and a series of breakout API containers, enabling seamless integration with other systems, across multiple enclaves.

+ Led the development effort as a product owner and chief developer of a digital, edge-processing, interface that enables machine learning at the point of data generation, reducing congestion at centralized operation locations for legacy field devices.+ Oversaw the design and development of a comprehensive multi-classification-enclave data service platform, including features for data ingestion, visualization, and analysis, and a supplementary Android app for remote control and monitoring. Built out a Kubernetes cluster to host the system and ensure scalability and reliability. + Successfully implemented generative adversarial neural networks (GANs) to generate synthetic data from a single event source, enabling the novel approach of training on one-time occurrence incidents. This breakthrough approach allowed for improved accuracy and efficiency in machine learning models.

+ Last Line of Troubleshooting: Served as the ultimate authority for diagnosing and resolving complex issues across multiple ICBM systems, ensuring readiness and reliability.+ Risk Management & Decision Making: Acted as the responsible official for approving high-risk, non-standard repair procedures and engineering waivers on nuclear sites, based on in-depth engineering expertise.

+ Led the development effort and contributed to the codebase for a Synthetic Aperture Radar (SAR) object detection solution, enabling highly accurate and efficient identification of objects in challenging environments. The innovative solution utilized complex algorithms and required expertise in both radar signal processing and machine learning as well as development of a software defined radio.+ Pioneered and implemented a telemetry-based application to determine the accuracy of weapons fire on Air Force test ranges, utilizing data from disparate radar sources. The innovative solution was adopted for use, enabling better evaluation and selection of munitions for use in the field, and delivered significant value to the Air Force.+ Contributed to the codebase for a networked silhouette-based auto targeting munition delivery system, utilizing advanced image processing algorithms and machine learning techniques to achieve real-time detection and targeting of silhouettes.