Corporate AI Service Lead -- deploy a scalable enterprise GenAI LLM inference service for thousands of users via vLLM, Docker, API gateways. Enable AI acceleration of software engineering development, assess and integrate vendor tools, size GPU hardware, mature AI offering roadmap, deploy AI analytics via python and SQL on AWS and Azure.
Technical lead — provide direction for data & AI/ML software services using Large Language Models (OpenAI API, Vector Databases, RAG), event streaming (Apache Kafka), graph data warehousing (Neo4j), and fullstack (Python, Node.js) technologies. Patent awarded in 2024.
Product management — drive value creation, prioritize product roadmap, execute agile tasking, negotiate customer requirements, and grow large engineering teams to execute rapid software prototypes.
Software Systems -- developed Cameo SysML Activity Diagrams, Use Case Diagrams, Internal Block Diagrams, custom profiles for large scale systems.
Reinforcement Learning Agent — Developing a transformative, cloud native reinforcement learning system applicable to diverse simulation platforms. Utilizing AWS SageMaker for GPU training jobs, hyperparameter tuning, and endpoint inference, Apache Airflow for MLOps, and TensorFlow for deep learning.
Knowledge Graph Analytics — Invented a graph data science platform using Node.js and Neo4j to analyze 400k model nodes and identify and reduce over 1K model based Cameo SysML systems engineering (MBSE) architecture defects for multi-billion dollar projects. Translated customer business needs into data science tasks for 5 software engineers, secured funding, and defended model results at customer meetings.
Computer Vision — created an image classification pipeline for 4 terabytes of image data. Used regex parsing via Python for data preprocessing, Keras for scratch training of LeNet architectures and transfer learning using pretrained VGG, ResNet, and Inception networks.
Natural Language Processing — created a sentiment analysis system utilizing Scikit-learn. Used stop word reduction, data balancing, regex replacement.
Anomaly Detection — programmed IOT embedded sensors via Python and C++, wrote bash scripts for automated data collection, used Scikit-learn for anomaly detection.
Developing a predictive analytics system utilizing IOT sensors and machine learning. Programmed Java plugins to connect a System Architecture Modeling Tool via OpenAPI calls. Developed descriptive architectures in UML, SysML, and UPDM to manage complex systems.
Programmed in LabVIEW to control NI DAQ cards to determine resonant frequencies of a wireless charger. Wrote hardware validation procedures.
Wrote standards procedures for composite pole pilots and updated various existing distribution standards procedures.
Created a stock calculator that uses HTTP calls to take data from the Alphavantage database and calculate a linear regression and stock return.
Created a image classifier via Tensorflow.js and Mobilenet.
Computer Science
August 2018 - May 2022Electrical Engineering - Computer Science Technical Breadth
GPA: 3.361
September 2012 - June 2016Python, Java, C++, JavaScript, HTML/CSS, Sklearn, UML, SysML, UPDM, git
Machine Learning, DIY Electronics, Foodie Adventures
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