Backend, data, and product-minded software engineer building practical systems with Python, FastAPI, Flutter, Firebase, applied ML, and multi-agent AI workflows.
I like working where software has to make messy real-world information usable: city rules, market signals, exam preparation, security data, APIs, automation, and decision support. My strongest work combines backend engineering, data pipelines, AI-assisted systems, clean product thinking, and enough frontend/mobile skill to ship the full experience.
- Backend APIs, rule engines, and data-driven services
- Multi-agent AI systems, orchestration, and decision pipelines
- Flutter apps backed by Firebase, Firestore, and Cloud Functions
- ML/data pipelines for classification, preprocessing, evaluation, and research workflows
- Product engineering: turning fuzzy requirements into usable, testable software
Multi-agent AI trading research system for BTC, ETH, SOL, and ZEC with a live Streamlit dashboard.
- Runs five specialist Claude sub-agents for technical, macro, sentiment, whale, and risk analysis
- Uses an orchestrator to combine parallel signals into paper-trading decisions and Coinbase-ready order flows
- Includes support-level limit entries, ATR-based stops/targets, trailing stop management, Telegram alerts, and backtesting tools
- Built with Python, Claude Sonnet, Streamlit, Coinbase Advanced Trade API integration, and Windows Task Scheduler automation
Real-time parking intelligence API for connected vehicles and driver-assistance workflows.
- Evaluates curb-side rules and returns
safe,caution, orblockeddecisions - Estimates ticket exposure from NYC parking-rule data
- Built with Python, FastAPI, NYC Open Data, unit tests, and Render deployment
Cross-platform exam-prep app for the Architect Registration Examination.
- Flutter app with practice tests, flashcards, progress analytics, and AI coach flows
- Firebase-backed architecture using Auth, Firestore, Cloud Functions, and offline bundled content
- Built for practical study workflows with NYC building-code emphasis
Machine-learning pipeline for real-world vulnerability detection research at CUNY MassLab.
- End-to-end preprocessing, feature engineering, model training, and evaluation
- Python data stack with pandas, NumPy, and scikit-learn
- Research-oriented workflow focused on reproducible data preparation and model comparison
| Area | Tools |
|---|---|
| Languages | Python, Dart, JavaScript, Java, C++ |
| Backend | FastAPI, REST APIs, Firebase Cloud Functions |
| AI Systems | Claude, multi-agent orchestration, decision pipelines, Streamlit dashboards |
| Mobile & Product | Flutter, Firebase Auth, Firestore, offline-first app patterns |
| Data & ML | pandas, NumPy, scikit-learn, ETL pipelines, model evaluation |
| Databases | SQL, SQLite, Firestore |
| Delivery | Git, Linux, Render, GitHub Actions, Windows Task Scheduler |
B.S. Computer Science, Brooklyn College, CUNY
Graduated December 2025
I am focused on backend/data engineering roles where I can build reliable APIs, automate complex workflows, and ship products that solve concrete problems. I am especially interested in systems that combine rules, data, maps, AI assistance, market intelligence, or developer-facing APIs.