Applied AI Engineering
Building AI agents, RAG pipelines, retrieval workflows, and evaluation systems.
Sydney, Australia • Open to AI, Applied AI, AI Solutions & FDE roles
Hello, I am
Jinhao Luo
AI Software Engineer
Building practical AI systems that connect data, workflows, and real business needs.
I build AI-enabled platforms, conversational agents, retrieval systems, and data-driven applications. My work combines applied AI engineering, full-stack development, stakeholder communication, and structured problem-solving.
JLAI / DATA / SYSTEMS
01UnderstandBusiness need
02RetrieveGrounded context
03OrchestrateAgent workflow
04DeliverUseful outcome
01 / Profile
About Me
I am an AI Software Engineer and Master of Computer Science graduate based in Sydney. I have experience building data-driven internal platforms, RAG-based conversational agents, recommendation systems, and full-stack applications.
My strength is working between technical implementation and real business requirements. I enjoy clarifying ambiguous problems, structuring workflows, building practical solutions, testing their reliability, and communicating technical decisions clearly to both technical and non-technical stakeholders.
My undergraduate background in mechanical engineering developed my structured problem-solving and analytical thinking. I later transitioned into computer science, artificial intelligence, and software engineering to build technology that creates measurable value for users and organisations.
Product-Oriented Development
Translating business requirements and user workflows into practical product features.
Customer-Focused Problem Solving
Investigating technical issues, communicating constraints, and improving solutions through feedback.
Selected results from my work at SunDAO Ventures.
02 / Work
Professional Experience
Building internal software where data quality, workflow design, and day-to-day reliability matter.
SunDAO Ventures
AI Software Engineer
Sydney, AustraliaBuilt and maintained internal platforms supporting startup discovery, fund and member management, intent matching, deal tracking, document workflows, reporting, and AI-assisted review.
Key contributions
- Reduced fund onboarding time by up to 70% by redesigning data capture, onboarding, and deal-review workflows.
- Supported onboarding and platform operations for 211 paid startup customers from a pipeline of more than 1,700 startups.
- Maintained two internal platforms across authentication, data access, storage, access control, deployment, and continuity.
- Structured startup, fund, member, intent, and match-status data for tracking, reporting, analysis, and future recommendation workflows.
- Designed intent-driven matching logic using needs, goals, urgency, stage, sector, and preference signals.
- Supported issue triage, documentation, data updates, access-control checks, and workflow improvements.
Internal workstreams
SunDAO Intelligence Platform
Startup discovery, fund and member management, intent matching, deal tracking, and AI-assisted review.
SunDAO Website
Redesigned and maintained the public website to improve navigation, structure, and onboarding.
Core Platform Maintenance
Authentication, database, storage, deployment, access control, and operational workflows.
03 / Selected work
Featured Projects
Three case studies across applied AI, product engineering, and evidence-based machine learning.
01
Professional AI and Data Platform
SunDAO Intelligence Platform
An internal data-driven platform supporting startup discovery, fund and member management, intent matching, deal tracking, document workflows, and AI-assisted review.
Practical learningStructured data and workflow design created a practical foundation for future matching automation.
02
AI Agent and RAG Application
MindPal - AI Mental Health Companion Agent
A conversational AI application combining agent orchestration, RAG, memory, crisis detection, fallback search, and offline evaluation.
Practical learningEvaluation, explicit safety routing, and layered memory are core system capabilities - not afterthoughts.
03
Machine Learning and Recommendation System
Heterogeneous Graph Neural Network Recommender System
A recommendation system for sparse Amazon - Reddit data, focused on rating prediction, relational modelling, explainability, and model comparison.
Practical learningThe negative result established an evidence-based suitability boundary for Meta-Path features in sparse, noisy graphs.
04 / Toolkit
Technical Skills
A practical toolkit spanning AI systems, data, product development, and delivery.
AI & Machine Learning
Programming & Data
Frontend & Full-Stack
Databases & Infrastructure
Product & Delivery
Tools
05 / Foundation
Education
February 2024 - December 2025
The University of Sydney
Master of Computer Science
Sydney, Australia
Distinction averageSeptember 2019 - June 2023
Nanchang University
Bachelor of Mechanical Engineering
Nanchang, China
GPA 3.2/4.0Received scholarships with distinction for three consecutive years.
06 / Contact
Let us Build Something Useful
I am interested in opportunities where I can combine applied AI, software engineering, data, and customer-focused problem solving. I am currently based in Sydney and open to discussing AI Engineer, Applied AI, AI Solutions, and Forward Deployed Engineer roles.