About Me

Forward Deployed AI Engineer at Pydantic with experience spanning software engineering, data science, MLOps, and LLM applications. I work with teams to build reliable agentic systems using Pydantic AI and Logfire, applying full-stack observability and continuous improvement patterns to production AI. My prior work includes machine learning infrastructure, reinforcement learning solutions, and recommendation systems driving $40M+ in revenue.

8+
Years Experience
MS
Data Science
Pydantic
Current Team
Seattle
Based

Education

Master of Science in Applied & Computational Mathematics & Statistics (Data Science)
University of Notre Dame
2018 – 2020
Bachelor of Science in Computer Engineering
University of Washington Bothell
2014 – 2017
Minors: Mathematics & Physics

Certifications

Reinforcement Learning Specialization
University of Alberta, 2021
Generative Adversarial Networks Specialization
DeepLearning.AI, 2021
Natural Language Processing Specialization
DeepLearning.AI, 2020
Deep Learning Specialization
DeepLearning.AI, 2018

Publications

  • The AI FDE Dilemma: Overpriced Engineers or Catalysts of Industry Change? — LinkedIn, August 2026
  • New Control Paradigms with Agentic Components — LinkedIn, July 2026
  • How to measure AI productivity: From LLM token costs to business value with Arize AX — Arize AI, July 2026
  • Coding agent tracing and evaluation: An open source tool to improve AI coding workflows — Arize AI, May 2026
  • Adaptive Guardrails for Agentic Applications — LinkedIn, 2026
  • Great Books for AI Engineering — Towards Data Science, 2025
  • Achieving Self-Service Onboarding for MLOps Tooling — Wayfair Tech Blog, 2023
  • Great Books for Data Science 2 — Towards Data Science, 2021

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