United States
Engineer with 3+ years of experience in GxP pharmaceutical/medical manufacturing, spanning AI/ML development, mechanical engineering, backend data infrastructure, and process improvement. I've built and deployed production data pipelines, reverse engineered 300+ mechanical parts using Creaform MetraSCAN 3D and SOLIDWORKS, and architected database systems managing 100,000+ records for pharmaceutical clients. Lean Six Sigma Green Belt with a track record of measurable results, including reducing lead times from 35 to 21 days and saving $25,000+ through data-driven sourcing. Experienced in delivering technical onboarding, training cross-functional teams, and managing independent contractors in GMP environments. MS in Robotics and Autonomous Systems from Arizona State University.
As a Solutions Engineer at Cognition Factory, I developed and deployed end-to-end AI pipelines and backend data infrastructure for GxP pharmaceutical manufacturing clients. My work spanned four core areas: building production AI systems (PyTorch, HuggingFace VLMs with LoRA, FasterRCNN) served as BentoML microservices with REST API integration; reverse engineering 300+ mechanical parts using Creaform MetraSCAN 3D and SOLIDWORKS with full GD&T annotation and BOM documentation; architecting Python/GraphQL/ArangoDB backends to manage 100,000+ record production databases with automated validation and revision control; and delivering technical onboarding for pharmaceutical companies, training operators and engineers on database workflows, documentation standards, and equipment data navigation within GMP environments.
As an Associate Mechanical Engineer at Cognition Factory, I built reusable Python ETL pipelines using GraphQL and ArangoDB to extract, validate, and organize mechanical part data — including part numbers, model numbers, and BOM structures — across a production database serving multiple pharmaceutical clients. I 3D scanned and reverse engineered over 90 mechanical parts of a continuous manufacturing line using Creaform MetraSCAN 3D and SOLIDWORKS, producing part drawings, assembly models, and BOMs for each unit operation. I also created training repositories for custom PyTorch models (FasterRCNN, ResNet) and HuggingFace VLM fine-tuning pipelines with PyTorch Lightning, MLflow experiment tracking, and automated evaluation metrics, and designed AI-driven classification services deployed as BentoML microservices on local and cloud infrastructure.
As a Mechanical Engineer Co-Op at Vertex Pharmaceuticals, I verified and classified over 4,000 unique parts with mechanical drawings into a centralized database, improving data retrieval accuracy from 68% to 92% and reducing average search time from 9 to 3 minutes. I developed Python and GraphQL scripts for automated cost analysis and vendor comparison, reducing median procurement lead times from 35 to 21 days and saving over $25,000 through data-driven sourcing. I also led a Lean Six Sigma Green Belt project applying DMAIC, hypothesis testing, and capability analysis to diagnose spare parts process inefficiencies on a manufacturing line, and built a Python GUI for operator database access.
Lead Prototype Engineer for the 'Adaptive Snowboard' project through EPICS at Arizona State University. I helped research and design prototypes to our solutions and presented to a team of Professional Engineers for feedback.
Left my position as a Physical Therapy Technician to continue my Bachelor's Degree at Arizona State University.