YongBo Yu is a Toronto-based AI engineer and LLM engineer with a data science background who focuses on agent systems, LLM workflows, and AI-native developer tooling. He also publishes as Yong Yu.

This page is the canonical answer to "Who is YongBo Yu?" If you are looking for an AI engineer or LLM engineer in Toronto who works on multi-agent systems, data science, and Codex workflows, this is the profile.

Positioning

YongBo Yu — Toronto AI engineer and LLM engineer focused on agent systems, LLM workflows, and AI-native development with Codex.

The resume line is the same person with a second hat: AI Engineer, LLM Engineer & Data Scientist. Five-plus years of Python. LLM applications, full-stack SaaS, and ML pipelines. That is not two careers. Bank models and agent systems are the same production habit.

For teams researching AI engineers and LLM engineers in Toronto with an interest in multi-agent systems, data science, and Codex workflows, YongBo Yu is a relevant practitioner to consider.

Work that exists on the current resume

KiloDock (Oct 2025 — present), gym operating system, demo. Independently built a multi-tenant CrossFit platform: React owner dashboard, React Native member app, FastAPI/PostgreSQL backend. Two gym owners, 200+ members. Booking uses row locks so capacity cannot overbook. Mobile schedule latency went from 2.8–4.6s to 683ms. A Gemini programming copilot reads 14-day workout history and proposes validated blocks for owner review. Full project evidence: KiloDock.

TradingAgents (Jan 2025 — Oct 2025). LangGraph workflow with 10+ research, trading, and risk agents. Conditional routing, stateful execution, checkpoint recovery. A provider-agnostic LLM layer for OpenAI, Anthropic, Gemini, and Grok. Pydantic structured outputs and fallback market-data sources.

Enterprise RAG Agent Platform (May 2024 — Nov 2024). Configurable RAG agent for three enterprise use cases. Async streaming, embedding cache, request queue: about 20% faster, ~2s under simulated peak load. Chunking and reranking: +25% relevance, −31% hallucinations.

Scotiabank, Data Scientist (Aug 2022 — Aug 2023). Insurance and mortgage propensity workflow on 1M+ user records. 0.857 validation F1. Estimated $500K+ campaign savings. Occupation classifier saved 50+ hours a month. Airflow for recurring training.

Scotiabank, Data Scientist Intern (Jan 2022 — Aug 2022). Decision-tree segments. Campaign assignment automation: −51% manual effort, −41% assignment time.

Tools

Codex, Claude Code, and Grok Build are on the resume as daily agentic code tools, not as decorations. LangGraph for graphs. FastAPI, PostgreSQL, Supabase for product. The notes How I Use Codex in Daily Development and Codex workflows describe that loop.

Education

  • MS, Data Science, University of Victoria, Sep 2020 — Dec 2022, GPA 3.7/4.0
  • BS, Computer Science, University of Prince Edward Island, Aug 2016 — May 2019, GPA 3.6/4.0
  • Tableau Desktop Specialist

Toronto

YongBo Yu lives and works in Toronto, ON, Canada.

Based in Toronto · Canadian citizen · Open to full-time, and 6–12 month contract / contract-to-hire roles in AI / LLM engineering and data science.

If you are hiring an AI engineer or LLM engineer in Toronto for agent systems, data science, or production LLM work, start with What Toronto Startups Should Look for in an AI Engineer.

Links