I’m a systems-focused engineer with experience spanning software, infrastructure, and applied AI.
I build reliable systems, automate workflows, and document real-world problems with clarity and intent.
My work sits at the intersection of engineering, operations, and intelligent systems, shaped by experience across technology, finance, and the arts.
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- 🔧 Focused on DevOps, automation, and systems reliability
- 🧠 Strong in structured troubleshooting and incident resolution
- 🧩 Experienced across support, infrastructure, and workflow platforms
- 🎨 Background in the arts, bringing clarity, adaptability, and communication to technical work
- 📍 Los Angeles, CA
I’m interested in roles that sit close to real systems, from technical support engineering and platform work to applied AI and automation.
Active Projects:
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Python Daily Practice App - A Kivy mobile app with FastAPI backend providing daily coding challenges focused on data cleaning, incident diagnosis, and automation workflows. Learning full-stack development with JWT authentication, PostgreSQL, and Railway deployment.
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MediTrack - Browser-based medication tracking application demonstrating Notifications API, Web Audio API, and localStorage persistence with vanilla JavaScript.
Learning Areas:
- Full-stack mobile app development (Kivy, FastAPI, PostgreSQL, Railway)
- Real DevOps automation with Terraform, CI/CD pipelines, and monitoring tools
- Advanced Python backend architecture and RESTful API design
- Frontend browser APIs and vanilla JavaScript patterns
- Agentic workflows and system orchestration frameworks
Languages
Python · JavaScript (ES6) · Bash · HTML · CSS
Frontend / Platforms
React · ServiceNow Platform (UI Policies, Client Scripts, Flow Designer) · TailwindCSS
Backend / Frameworks
Django · Node.js · Express (working familiarity)
Databases
PostgreSQL · SQLite (dev/test)
DevOps / Tooling
Jenkins · GitHub Actions · Terraform · Docker · Prometheus · CI/CD Pipelines
Infrastructure / Systems
AWS (EC2, S3, IAM fundamentals) · Linux · Cloudflare (failover & availability concepts)
AI Image Detection App
End-to-end machine learning pipeline and supporting cloud infrastructure for classifying AI-generated images. Covers model evaluation, system design, and deployment considerations.
RealEyez (Team VerifEyez)
Winning team project from Kura Labs Cohort 5 (2024). A production-oriented ML system designed to detect whether an image is real or AI-generated, with emphasis on reliability, evaluation metrics, and real-world usage constraints.
Building the PepsiCo Agentic Landscape
Reference architecture for an enterprise-scale agentic system using ServiceNow AI Agent Studio, n8n, and MCP to automate logistics incident resolution. Presented at ServiceNow World Forum NYC.
Agentic Logistics Incident Response (PepsiCo)
Automated supply chain incident processing system that analyzes financial impact from truck breakdowns, determines optimal remediation paths, and coordinates execution through external systems.
EC2 Remediation System (Netflix)
Semi-automated incident response system built as a ServiceNow Admin and Junior Developer to help DevOps engineers remediate failing AWS EC2 instances, improving response time and protecting streaming reliability.
Measuring AGI Performance
A technical overview of the ARC Prize’s Artificial General Intelligence benchmark, including scoring mechanics and evaluation methodology, written to clarify complex research concepts.