Justin Zhao

I build LLM systems, agentic workflows, and AI products for real users and operational settings.

I have been developing software for 8 years, and have focused on NLP and AI infrastructure for the last 4 years. My works include LLM workflows and agentic frameworks for both research and production, with products that have earned 1000+ stars on GitHub and scaled to millions of user interactions across platforms. Across this work, a consistent principle is that robust AI systems require systematic evaluation, reproducible benchmarking, and bias-aware validation before scale. As a Ph.D. Candidate finishing up my dissertation, I am looking to own projects that are truly innovative and impactful, scale to substantial real-world usage, and drive tangible commercial value.

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Timeline

From a price monitor script in 2019 to production LLM systems and AI products today, each phase added engineering depth, product experience, and operational rigor.

2026

Democratizing LLM Development: NoCode Platform

Architected a graph-based multi-agent orchestration framework, replaced 4 weeks of training with minutes of drag-and-drop on a canvas to build customized LLM workflows, and benefited 1200+ users monthly; listed as a trending space on Hugging Face. Reduced processing time by 86% through model routing and by dispatching concurrent LLM batches through Redis-backed Celery workers. Solved bugs reported by users and balanced requests against available resources to keep the product meeting real community needs. 500+ stars on GitHub. 70+ stars on Hugging Face.

2026

Price Monitor 2.0: Agentic Trading System

Lowered the barrier to quantitative trading for non-technical users by architecting an agentic trading framework with customizable orchestration layers that let agents autonomously fetch data, build context, make and review decisions, and cooperate with each other. Coached 500+ non-technical users by demoing platform mechanics and risk analysis during high-volatility periods, presented the product to prospective investors, and translated feedback into product iterations. 200+ stars on GitHub.

2025-Now

Research Journey

Ph.D. Research Assistant, National Science Foundation, University of Texas at Dallas. Automated a data cleaning pipeline using LLMs to process 10GB+ of noisy corpora, reducing human annotation time by 95% and converting a 3-month manual labor into 4 hours of parallelized GPU computation on HPC. Designed an AI model evaluation framework to measure LLM biases, providing guidance for 200+ researchers to use LLMs responsibly. Coached 5+ early-career researchers on LoRA and RLHF best practices, partnered with multiple research groups to translate requirements into technical specifications, and established annotation and data collection guidelines for international collaborators.

2025-2026

M.S. Computer Science

University of Colorado Boulder training in machine learning and cloud computing, applied to the systems and products built above.

2022-2024

Research Assistant, University of Texas at Dallas

Ran quantitative and text-as-data pipelines for research groups, covering data collection, knowledge mining, visualization, and machine learning for text.

2021-2022

Operations Analyst, TikTok, ByteDance

Optimized advertising strategies for stakeholders by making data-driven decisions, increasing traffic for targeted accounts by 300% on average. Built automated pipelines to record, analyze, and market accounts and campaigns, reducing 30 hours of weekly manual work to 1 hour per week. Led a cross-functional task force to manage client requests during campaigns, translating urgent needs into operational solutions, keeping deliverables on track under pressure, and synthesizing outputs into stakeholder-ready insights presented to senior leadership.

2019-2020

Where It Began: Price Monitor, 2019

Built the first iteration of Price Monitor in 2019, a rule-based system for tracking prices and surfacing actionable signals. The same core problem that drove the 2019 version, making market information legible and actionable without constant manual monitoring, is what the 2026 agentic system solves at a fundamentally different level of autonomy.