AI Deployment Engineer / FDE
I build the full reliability layer around frontier model calls: multi-provider orchestration, retrieval grounding, on-chain execution, and provable safety gating. I verify every claim with seeds, logs, and receipts, and I treat my own results with a reviewer's skepticism.
Agent safety & deployment
FDE & AI roles
Every figure traces to a seed, a log, and a result file. Click a number to jump to the system that produced it.
Most AI projects die between the demo and the deploy. I build for the part after the demo: systems that do real work, fail safe under pressure, and prove it.
Multi-provider orchestration (OpenAI, Anthropic, local) with automatic failover, content-hashed caching, and per-call cost, latency, and token telemetry.
Safety state machines specified and model-checked in TLA+, so the kill-circuit is proven safe, not hoped safe.
A Q-learning adversary I use to attack my own systems, with the failure modes documented honestly instead of hidden.
Every reported number traces to a seed, an LLM trace, and a result file. One command reruns everything. I document the limits of my own evidence.
Also available for AI deployment delivery work through Lion Protocol: one scoped workflow taken from problem to a live, monitored, owned system, typically in about 90 days.
Built solo, end to end. Each one ships with the measurements and the means to reproduce them.
A multi-provider agent stack with a formally verified safety circuit.
Code available on request. Deep dive →
Three LLM roles argue, then the verdict is enforced on-chain.
Code available on request. Deep dive →
Trust-weighted mean-variance control for stablecoin pegs, accepted at IEEE ICBC 2026.
Code available on request. Deep dive →
On-chain SNARK verification at a fraction of incumbent cost.
Code available on request. Deep dive →
Want the full problem, motivation, and contribution walkthrough for each system? Visit the Projects page →
My PhD work sits at the intersection of autonomous agents, consensus design, and safety. The engineering and the research feed each other.
How do you trust the output of an autonomous agent? My research develops a proposer, challenger,
judge architecture, where independent agents argue and adjudicate before a decision is acted
on, as a path to calibrated, defensible automation. The same chassis I study academically is the safety
layer I deploy in real systems: outputs are challenged and judged, not blindly executed.
Themes: multi-agent systems, consensus and trust, calibrated forecasting, blockchain and
decentralized-system safety.
From open-source infrastructure at The Linux Foundation to shipping AI agent systems today.
5 years of hands-on cross-border trade operations plus 5 years of multi-agent AI research and engineering, which is why the agents I build work for real Greater China trade businesses, not just in a demo.
Lion Protocol · Hong Kong
Design and ship AI agent systems: multi-provider orchestration, retrieval grounding, and safety gating, served through FastAPI with observability and human-in-the-loop checkpoints built in. Scope, build, harden, and hand over systems clients own outright.
Family Export Business · Greater China
Cross-border sourcing, supplier coordination, and export operations across multiple markets. Hands-on operational experience that grounds the AI agents I build in real B2B trade workflows.
School of Business, Hong Kong Baptist University
Guest lecturer on a graduate-level financial-computing course, teaching students to build and backtest quantitative models in Python (NumPy, pandas, scikit-learn). Based in Hong Kong under the Top Talent Pass Scheme.
The Linux Foundation · San Francisco, CA
Built a real-time performance dashboard for Hyperledger Fabric using the MERN stack and authored Caliper-CLI documentation that cut developer onboarding time. Work presented at Hyperledger Global Forum 2020.
Purdue University, CSE · West Lafayette, IN
Built a secure Hierarchical-Deterministic (HD) wallet in Go and open-sourced it. Implemented an adversarial ML attack on CNNs and presented findings to the university security community.
PhD Candidate, Computer Science
University of Notre Dame
Advisor: Jarek Nabrzyski
Dissertation on trust and systemic risk in decentralized systems, and multi-agent architectures for calibrated, safety-aware automation.
M.S., Computer Science (Information Security)
Purdue University
2018 to 2019
Information security, cryptography, and adversarial machine learning.
B.A., Mathematics
University of Washington
2014 to 2018
Applied mathematics with a focus on machine learning and NLP.
Primary focus. OpenAI, Anthropic, Crypto.com.
Binance, OKX, Crypto.com.
OpenAI DevX, MiniMax, Mistral, Qwen, DeepSeek, Zhipu.
Also building a Mandarin-first course, "Your First AI Employee", and a weekly Skill Studio at Lion Protocol OPC. The cadence is make, document, share, sell.
Open to AI Deployment / FDE roles, AI and crypto engineering, DevRel, and selective consulting. I read every message.