Pat Fury

About Pat Fury

AI/ML Platform Engineer at JPMorgan Chase, focused on enabling internal researchers through scalable distributed compute, high-performance GPU access, and secure, containerized JupyterHub environments. I contribute across the stack—from GPU orchestration and Docker image optimization to designing front-end tools such as our GPU reservation UI that improves access to limited compute resources.I’ve been working on an internal Notebook Intelligence extension for JupyterLab, enabling ML researchers to leverage enterprise-approved, paid language models (e.g, ChatGPT and Claude) directly within their notebook workflows to accelerate experimentation, analysis, and iteration. In parallel, I help drive platform modernization across VS Code, Python 3.12, JupyterLab, and Ray. Our team collaborates closely with the internal AI SDK group and operates within the Fusion platform—JPMorgan’s ecosystem for AI/ML—to ensure tooling is reliable, extensible, and aligned with evolving ML workflows.Outside of core development, I care deeply about education and mentorship. I support junior engineers through formal and informal mentorship, advocate for better internal documentation, and am driving new user decision matrices that help teams compare available tools (e.g, internal platforms vs. SageMaker or Databricks) and choose the right solution for their use case.I also serve as a Psychological Operations Sergeant in the U.S. Army Reserve, where I lead with clarity under pressure and think deeply about systems of influence—lessons I carry into engineering, AI ethics, and team dynamics. I plan to complete my Army service in June 2026.My long-term goal is to help build AI infrastructure that remains grounded in human needs and responsibility. Until then, I focus on building systems that scale—and serve.

Experience

1. Software Engineer Ii - Ai Ml Platform

   JPMorganChase

   Jun 2025 — Present · McLean, VA, US

   Supporting internal AI/ML research workflows by building and maintaining distributed compute infrastructure on JPMorgan’s Fusion platform. Our team enables high-performance GPU access through containerized JupyterHub environments, ensuring reliability, scalability, and user experience for data scientists and ML engineers across the firm.• Contributing to Docker image optimization and GPU orchestration pipelines for secure, scalable model training and experimentation• Designing and implementing a front-end GPU reservation UI to streamline access to limited compute resources• Pushed integration of GitHub Copilot into container workflows and advocated for modernization across Python 3.12, JupyterLab, Ray, and VS Code tooling• Collaborating with the internal AI SDK team to align platform capabilities with evolving machine learning workflows• Driving documentation and onboarding improvements, including user decision matrices to compare internal platform options (Fusion, SageMaker, Databricks) and guide tool selection• Mentoring junior engineers and contributing to internal knowledge sharing as part of a culture of education and self-service enablementFocusing on systems that not only scale, but serve. Building toward long-term impact in model behavior, alignment, and safe AI deployment.

Education

• Rochester Institute of Technology

  Physics, Philosophy

  2013 — 2017

• New Jersey Institute of Technology

  Bachelor of Science - BS, Electrical and Electronics Engineering

  2021

Skills

• Powder X-Ray Diffraction
• Scanning Electron Microscopy
• Strong Work Ethic
• Philosophy
• Monochromator
• Matlab
• Teamwork
• Leadership
• Autocad
• Physics
• Python
• Optical Microscopy
• Latex
• Orcad Capture Cis
• Circuit Analysis
• Microsoft Word
• Java
• Mathematica
• Labview
• Critical Thinking
• Data Analysis
• Microsoft Excel
• Writing
• Research
• Autodesk Inventor
• Microsoft Office