AI Agent Engineer · Full-Stack Developer — CS @ York University · Toronto, Canada
I turn AI agents from demos into production-grade systems — my favorite part is the unglamorous engineering that makes them reliable, observable, and affordable.
What I'm good at
- Agent orchestration — LangGraph state machines, typed tool contracts, bounded workflows that turn free-form agents into controllable pipelines
- LLM reliability — dual-layer guardrails (cut non-compliant output ~15% → <2% in production), fail-closed integrity gates, rule-engine-verified numbers, full audit trails
- Cost & performance — input-hash caching (60–70% hit rate, <50 ms), agent regression evals (~10% fix-pass lift, token cost ~2M → ~70k per task)
- 0→1 delivery — SwiftUI / Next.js / FastAPI / Spring Boot with CI/CD and monitoring; taking products from concept to production, solo or in small teams
What I'm into — agentic product systems · applied LLM infrastructure · AI + full-stack delivery · developer knowledge platforms
- 🏆 Adventure X 2026 — Track 1st Prize: Possibility, AI decision companion — solo build, 6 days, 189 commits, concept → shipped product
- 📜 Anthropic Claude Certified Architect — Foundations (CCA-F)
- 🔧 Merged PR in OpenClaw (385k★) — PR #2143: fixed model failover retrying cooled-down OAuth channels (worst case ~1 h user-facing stall → instant recovery)
SR&ED claim console with evidence ingestion, structured pre-checks, and audit-friendly T661 drafting workflow for founders and reviewers.
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Engineering knowledge base covering CS core, AI agents, backend/frontend, and DevOps — built as a production-ready documentation platform.
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Self-hosted GitHub maintenance bot — issue triage, bug reproduction, auto-fix, code review, Q&A. Bounded workflows + regression eval: ~10% fix pass improvement, token cost ~2M → ~70k per task.
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AI decision companion for young professionals navigating career transitions. A 3-stage funnel: streaming AI chat surfaces emotional context & decision signals → structured matching with 3 real human experiences across different outcomes with rationale → paid conversion. Built solo in 6 days (189 commits) from concept to shipped product. |
AI fitness coaching experience focused on adaptive training + nutrition logic, progress analytics, and coach-assisted personalization.
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Agentic AI dietitian that combines receipt/fridge/meal perception with goal-aware planning to reduce waste and deliver actionable meal decisions.
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Base Pay bill-splitting app for group settlements with USDC, real-time status tracking, share links/QR flows, and optional NFT receipts.
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| Agent / LLM | LangGraph · LangChain · RAG · MCP · Structured Output & Guardrails · Agent Eval & Cost Control |
| Languages | Python · TypeScript / JavaScript · Java · Swift · SQL |
| Backend & Frontend | FastAPI · Spring Boot · Next.js / React · SwiftUI · PostgreSQL / pgvector · Redis · Celery |
| DevOps / Cloud | Docker · GitHub Actions · AWS · Vercel · Linux |
Turning AI agents into production systems with measurable outcomes.