Bridging technical AI/ML capabilities with real-world business results. 20+ years across logistics, B2B technical sales, and entrepreneurial consulting—now applying AI to solve complex problems across diverse domains.
I bridge technical AI/ML capabilities with real-world business applications.
With over 20 years of experience across logistics operations, B2B technical sales, entrepreneurial consulting, and security systems, I bring a unique perspective to AI/ML solutions engineering. My diverse background has developed essential skills: translating technical features into business value, managing client relationships, coordinating cross-functional teams, and rapidly learning new domains.
Currently completing TripleTen's AI/ML Engineering bootcamp, I specialize in understanding client business problems, designing AI/ML solutions that address real-world constraints, and managing implementation projects from discovery to deployment.
My unique combination allows me to speak both technical and business languages fluently—ensuring AI/ML implementations that drive adoption, demonstrate value, and deliver measurable results. View my full resume →
Years of Professional Experience
Solutions Engineer
Problem Solver
AI/ML and data science projects demonstrating technical problem-solving and business value delivery
Autonomous agent pipeline that turns a folder of trucking company CSVs into a complete, deployed sales demo — insights report, company profile, and a live ops platform — with no human steps between input and output. Five-phase Claude Code pipeline: data generation, consultant-style analysis, three polished deliverables, and VPS deployment over HTTPS.
A self-hostable job application tracker where every interaction—adding listings, updating statuses, recording notes—happens entirely through conversation with an embedded Claude AI assistant. Real-time dashboard with a live-updating table, rich detail panel, and color-coded alert callouts. Built on FastAPI with a vanilla JS frontend.
Analyzed 35+ years of gaming industry sales data to forecast 2017 product success across platforms, regions, and genres. Built statistical models to identify success patterns, then translated complex findings into clear marketing recommendations that non-technical teams can act on immediately.
Interested in working together? Let's connect.