Computer Science @ York University · Full-Stack & AI/ML Engineer · Toronto, ON

I build scalable systems — from AI agent platforms to real-time data pipelines. Passionate about turning complex problems into clean, impactful software.

Understand any codebase in minutes. Paste a GitHub URL and chat with the repo via RAG, with real file/line citations and a dependency-graph visualization.

• Architected a full-stack RAG system indexing GitHub repos via Tree-sitter AST parsing (function/class-level chunking) across Python, JS & TS, enabling cited, hallucination-resistant Q&A over codebases up to 10,000 files
• Designed a provider-swappable AI layer (Embedder/LLMClient interfaces) — local open-source models in dev, OpenAI in production, via config alone
• Built a production cost-control system — Cloudflare Turnstile + atomic Redis dual rate limiting — powering a public zero-signup demo with bounded API spend
• Deployed a 5-service backend (FastAPI, Celery, ChromaDB, PostgreSQL, Redis) on Railway with SSE progress streaming + a Next.js frontend on Vercel, covered by 230+ tests

FastAPI Celery RAG Tree-sitter ChromaDB PostgreSQL Redis Next.js TypeScript Docker

End-to-end ML pipeline for real-time stock analysis with an interactive Streamlit dashboard.

• Engineered data pipeline improving processing efficiency by 40%
• Reduced manual data collection effort by 90% via Yahoo Finance API automation
• Built interactive real-time visualization dashboard for faster, data-driven trading decisions

Python Machine Learning Streamlit Yahoo Finance API

Full-stack desktop app for movie discovery with personalized watchlists and real-time TMDB content sync.

• Designed and optimized MySQL relational schema, cutting data retrieval latency by 30%
• Built full authentication system with personalized watchlists and review management
• Integrated TMDB REST API for dynamic, real-time content updates and enhanced UX

Java Swing MySQL TMDB API

AI/ML Intern — Automators Lab (May 2025 – Sep 2025)

• Built multi-agent AI systems with LangGraph & LangChain, integrating GPT, Claude, Gemini & LLaMA models
• Developed 10+ custom Model Context Protocol (MCP) servers connecting agents to APIs, databases & business apps
• Engineered RAG pipelines for context-aware assistants and document-intelligence workflows

• CS Hub @ York — Full-stack dev & cloud workshops, hackathons, peer collaboration

"Build things that matter. Measure the impact. Iterate."