How I Built an AI Agent That 93% of My Company Actually Uses
Most enterprise AI tools die the same death: a flashy launch, then silence. Here's how a push-based design pattern broke that cycle — 46,000 conversations and counting.
01Shipped a habit-tracking social app to iOS with 26 row-level security policies covering every database table. Real-time messaging, friend system, and on-device TensorFlow.js inference for AI-powered streak insights — all running through Supabase with zero exposed endpoints.
26 RLS policies | On-device ML inference | iOS-deployed
Built before the interview, not after. Designed an AI-powered Slack monitoring system with Z-score anomaly detection and GPT-generated campaign insights. Closed-loop feedback lets the model improve from analyst corrections in real time.
Built proactively — secured the role before the first interview
Co-founded a mobile app for roommate coordination with end-to-end encrypted data. Built a consensus-driven AI framework inspired by Waze — LLM-powered task aggregation resolves household conflicts through anonymous proposals.
E2E encryption | LLM consensus engine | Anonymous proposals
Associate, Innovation & Automation
2025 - Present
New York, NY
Co-Founder & Lead Developer
Feb 2024 - 2025
New York, NY
AI & Cybersecurity Intern
Sep 2024 - Dec 2024
New York, NY
Cybersecurity Auditor
Dec 2021 - Aug 2023
New York, NY
M.S. Cybersecurity
Aug 2022 - June 2025
GPA: 3.96
B.S. Computer Science
Sep 2018 - June 2022
GPA: 3.90
I publish when I learn something worth sharing.
Looking for roles where I can secure AI systems at scale — guardrails, red teaming, model governance, or building the infrastructure that makes AI safe to deploy.
Also open to collaborating on AI security research and open-source tooling.