Bosheng (Daniel) Zhang
AI/ML Engineer · Solution Architect
Designing and delivering end-to-end AI systems — from computer vision pipelines to LLM-powered platforms. Specialized in AI agents, RAG, and production AI solutions.
About Me
Hi, I'm Bosheng Zhang, but you can also call me Daniel. I'm an AI engineer based in Brisbane, Australia, with 4+ years of experience designing and delivering end-to-end AI systems — from problem scoping to production deployment to stakeholder-facing dashboards.
At Vision HQ, I was deeply involved in architecting AI solutions for Australian local governments: computer vision pipelines monitoring thousands of kilometres of roads, city-wide cleanliness scoring systems, and an LLM-powered monitoring platform orchestrating data retrieval, anomaly detection, and automated reporting on GCP.
My strength is connecting the dots across the full solution lifecycle — data pipelines, model optimization, cloud infrastructure, API integration, and delivery that non-technical stakeholders can actually use. Currently deepening my work in agentic AI, RAG architectures, and domain-specific LLM applications for enterprise use cases.
Experience
AI Engineer & Researcher
Independent
- Researching and developing domain-specific business chatbots powered by LLMs, focusing on practical enterprise applications
- Exploring agentic AI frameworks, RAG architectures, and multi-modal models for real-world solution design
- Experimenting with emerging AI patterns through personal projects and prototypes
AI/ML Engineer
Vision HQ
- Deeply involved in architecting an AI-powered road monitoring system deployed across 5+ councils covering 5,000+ km of roads, achieving 95% detection accuracy and significantly reducing survey costs by eliminating dedicated survey vehicle deployments
- Helped design and develop a street waste detection system generating city-wide Cleanliness Index scores, enabling data-driven cleanup dispatch and equitable resource allocation
- Built an LLM-powered IoT monitoring platform on GCP using function calling to orchestrate Elasticsearch data retrieval, anomaly detection, and automated reporting — significantly reducing daily telemetry review time
Bachelor of Computer Science
University of Queensland
- Major in Machine Learning, with coursework in advanced algorithms, pattern recognition, and computer systems
- Completed course projects in medical image segmentation and reinforcement learning
Skills
AI / Machine Learning
Cloud & DevOps
Languages & Frameworks
Data & Tools
Featured Projects
Production AI/ML systems and selected research projects. Click any card to read the full case study.
AI-Powered Road Infrastructure Monitoring System
Computer vision system mounted on waste trucks for automated city-wide road defect detection, enabling proactive infrastructure maintenance.
AI-Powered Street Waste Detection & Cleanliness Index
Computer vision system that detects street-level waste and generates a city-wide Cleanliness Index, giving councils a data-driven view of urban cleanliness.
LLM-Powered IoT Device Monitoring Platform
Intelligent monitoring platform using LLM function calling to orchestrate data retrieval, analysis, and automated report generation for IoT device fleets.
Academic & Research Projects
Skin Lesion Segmentation with Improved U-Net
Deep learning model for melanoma detection achieving 0.8+ Dice score on the ISIC dataset, using an improved U-Net architecture.
GreenMiles - Sustainable Transportation App
React Native mobile app incentivizing public transportation through gamification, carbon footprint tracking, and social connectivity.
LaserTank AI Solver
AI solvers for the LaserTank puzzle game using A* search, MDP planning, and reinforcement learning algorithms.
CANADARM Motion Planning
Probabilistic Roadmap-based motion planning for the ISS Canadarm2 robotic arm, navigating obstacles in constrained 2D workspace.
RUSHB Network Protocol Suite
Custom network protocol implementation with server, adapter, and switch components for reliable data transmission across networks.
Blog
Thoughts on AI, engineering, and building things.
The AI Engineering Landscape in Spring 2026: What You Need to Know
A comprehensive knowledge guide covering frontier model releases, agentic AI, MCP vs function calling, the evolution of RAG, edge AI with small language models, and what it all means for AI engineers.
Harness Engineering: The Discipline That Makes AI Agents Actually Work
A deep dive into harness engineering — the emerging discipline of designing systems, constraints, and feedback loops that make AI agents reliable in production. Covers core architecture, real-world case studies, and practical implementation.
Welcome — Why I Started This Blog
A brief introduction: who I am, what I've built, and what you'll find on this blog about AI/ML engineering in production.
Let's Connect
I'm always open to discussing AI/ML projects, collaboration opportunities, or just connecting with fellow engineers. Feel free to reach out!
ddaniel.zhang0413@gmail.com