Applied AI / Full Stack Engineer
I build LLM agents, data agents, RAG systems, AI developer tools, and reliable AI applications — grounded in deterministic guardrails and evaluation rather than prompt-only trust. Currently pursuing a Master of Computing in Applied AI at NTU, Singapore; previously worked in AI/ML education before moving into applied AI engineering.
A runnable BI data agent: a natural-language question becomes validated read-only SQL, an executed result, a chart, and a reviewable trace. Zod-validated request/response contracts, export guardrails, and a versioned regression eval harness with deterministic scoring — the core path runs without an LLM API key.
A runtime permission gateway for AI agent tool calls: the model proposes an action, deterministic code decides. Zod-validated policy engine returning ALLOW / APPROVAL / DENY, SOP-to-policy compilation, and an MCP-based tool-call integration.
Verified session handoffs for long-running Codex CLI agents, rebuilding continuation state from git and test evidence instead of a chat summary. Installable CLI with CI and tests; only hands off at a safe turn boundary, with a bilingual README.
A cross-app memory and context layer for personal AI agents that gates what gets remembered instead of storing everything. MemoryGate scopes every record by person, task, evidence, privacy level, and TTL before it can reach a reply.
A from-scratch, decoder-only Transformer (nanoGPT-style) trained on character-level text. Full training loop, causal self-attention, and autoregressive sampling implemented directly in PyTorch.
Python · TypeScript · JavaScript · React · Next.js · FastAPI · SQL · LLM APIs · RAG · Agent Systems · MCP · Git
- Master of Computing in Applied AI, NTU — Singapore
- Focused on applied AI / agent engineering: data agents, RAG, reliable AI application design
- Open to Applied AI / AI Engineering opportunities
- Portfolio: haopan036.github.io
- LinkedIn: in/hao-pan-59193937a


