Toronto · AI Systems · Quant Infrastructure

Where AI, quantitative engineering, and distributed systems meet.

I am Xinyue Xiang, a quantitative developer working across AI systems, quantitative infrastructure, distributed platforms, and data-intensive engineering. My work combines front-office rigor, applied machine learning, and production-grade system design.

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Senior Quantitative Developer at RBC Capital Markets
Research collaborations spanning Ivey, CMU, Zhejiang, and UAlberta
Background in Python, C++, Java, cloud systems, and applied ML

Focus

Engineering depth with research range.

I work at the boundary of quantitative systems, applied research, and platform delivery. The common thread is building tools that are rigorous enough for complex environments and clear enough for people to trust.

Quantitative engineering

Pricing and risk systems, HPC workloads, scenario analytics, and production services designed for trading environments.

Applied AI and research

LLM workflows, RAG pipelines, NLP, and research tooling built for real questions instead of demo-only prototypes.

Large-scale product delivery

Enterprise modernization, payment platforms, cloud migration, and developer-minded execution in regulated environments.

Skills

Systems, research, and delivery capabilities in one place.

Programming & Data Science

Python Pandas NumPy Scikit-learn PyTorch TensorFlow SQL Java Machine learning NLP Predictive modeling Reinforcement learning Data visualization Google BigQuery

AI & LLMs

OpenAI API LangChain Prompt engineering RAG LoRA Entity recognition Topic modeling

Cloud & Infrastructure

Docker Kubernetes AWS SageMaker AWS Lambda EC2 RDS DynamoDB S3 Serverless architectures Microservices Airflow ETL pipelines Messaging middleware

DevOps & Full Stack

Jenkins GitLab CI Travis CI Git Jira Confluence Linux/Unix Agile/Scrum Django Node.js ReactJS AngularJS REST API MongoDB MySQL SQLite

How I Work

The way I keep growing while I build.

The details of my resume live on the About page. What matters more here is how I think and how I keep moving: growth mindset, technical range, serious execution, and a willingness to keep learning in public and in production.

Growth mindset as a default

I care a lot about growth mindset. I try to stay open to new domains, harder systems, better habits, and the kind of feedback that actually raises the bar.

Range with real depth

I like being stretched across quant infrastructure, AI systems, cloud platforms, and research work, but I do not like shallow buzzword range. I want the work to be technically defensible.

Curiosity that becomes execution

I am naturally curious, but I do not want curiosity to stay abstract. The goal is to turn that curiosity into systems, tools, migrations, research outputs, and work that people can rely on.

Long-term compounding

I am drawn to things that compound over time: technical foundations, research habits, platform thinking, and the steady process of becoming better than I was a year ago.