AI/ML Technical Leader
I help organizations build AI systems that actually ship. For the past 8 years, I've specialized in engineering ML for safety-critical applications—DoD edge deployments, healthcare conversation intelligence, IoT monitoring systems—building systems your entire team can maintain, not just ML specialists.
Proven Impact
2wk → 4hr
Retraining Time
Reduced ML model retraining cycle from 2 weeks to 4 hours for DoD edge-deployed computer vision threat detection system.
10→10+/day
Patient Throughput
Scaled mental health assessment platform from 10 patients per month to 10+ per day while reducing evaluation time from weeks to minutes.
+15%
Model Accuracy
Improved model accuracy while cutting ML pipeline delivery time from weeks to days through systematic data quality improvements.
Services
Design and deploy ML pipelines that actually ship. Edge computing, model serving, continuous training, and monitoring at scale.
Rebuild engineering organizations, hire technical leadership, and establish processes that deliver high-quality software products.
Engineering AI for defense, healthcare, and other environments where reliability isn't optional. Security clearances held.
Part-time Head of AI/ML for organizations at the inflection point between prototype and production-ready systems.
Background
2023 – 2025
mpathic
Led ML platform development for real-time conversation analytics in healthcare and life sciences. Built Transformer-based text classification pipelines, designed OpenAI API applications for zero-shot behavior identification, and established AI organizational practices.
2018 – 2022
Software Engineering Institute, Carnegie Mellon University
Led strategic direction for AI Engineering in mission-critical DoD applications. Managed 7 managers and 40 direct reports. Built edge AI pipeline for computer vision threat detection deployed with US Army. Held TS-SCI clearance.
2016 – Present
QOE Labs
Fractional technical leadership for startups and established companies including BCG, CMU, and multiple healthcare/IoT ventures. Rebuild engineering teams, architect systems, and advise on AI/ML strategy.
2014 – 2016
Rhiza (acquired by Nielsen)
Led technical transformation from prototype to acquisition-ready product. Implemented continuous deployment, managed cross-functional teams of 9-15 engineers.
2000 – 2010
IBM
Technical lead for enterprise web applications including IBM.com Support Portal. Led architecture teams of 3-30 across multiple locations. Developed SOA governance and 3rd-party integration frameworks.
Thought Leadership
Policy Paper
Federation of American Scientists, 2025
Research Paper
arXiv, 2022
Article
InfoQ, 2021
Patent
US Patent 8,255,493
Working on ML in production, building technical teams, or deploying AI in safety-critical systems? I'm always happy to discuss interesting problems.