I build the production-grade market data pipline and tools that allow pricing, risk and trading decision to be made safely

Front-office focused Quant Developer with experience building trading tools and data systems for pricing, risk, and PnL workflows.

My work centers on developing scalable market data pipelines, configurable pricing and risk engines, and interactive applications that support traders in daily decision-making. I have hands-on experience integrating multi-vendor data sources (e.g. Bloomberg, Markit), implementing dynamic formula evaluation frameworks, and delivering reliable, production-grade systems.

I specialize in delivering high-impact tools that trading desks depend on, optimizing data workflows, reducing manual work, and enabling faster decision-making in fast-paced trading environments.

Current Focus at Investment Bank:

• Quantitative Tool Development: Built configurable calculation engines and analytical tools supporting trading desk workflows with dynamic formula evaluation and scalable metric generation
• Front-Office Platform Architecture: Architected end-to-end Python web applications with Enaml + Atom frontend, processing output of model data, reducing analysis time from hours to minutes
• Data Quality & Explainability: Developed rule-based explainability systems for metrics, with interactive dashboard interfaces and automated rule creation, reduced daily manual investigation time by 20-30 minutes per analyst
• Trading Data Pipelines: Optimized data pipelines for daily batch processing with focus on reliability, scalability, and trader usability

My Edge:

• Deep understanding of what traders actually need not theoretical infrastructure, but practical tools
• Full-stack expertise: Python backend + Enaml/Atom frontend + AG-Grid + Plotly
• Now expanding into AI Agents & LLM-powered automation to make trading tools even smarter

Four personal projects built in spare time, applying AI Agents to real trading workflow problems I encounter daily at work.

Implementation timeline:

P1:  ████████ (8w)
P2:          ██████████ (10w)
P3:          ████████ (8w, overlaps P2)
P4:                    ████████████ (12w, starts after P1+P2+P3)

📂 Full project details, architecture, and tech stack: ai-agents-quant-tools →

MSc Computing Science | University of Glasgow, UK (2021–2022)

• Dissertation: Automation in Quantitative Strategy Systems
• Core courses: Data Science & Systems, Machine Learning & AI, Information Visualization

B.Eng Software Engineering | East China University of Technology, China

• High-distinction grades: Software Engineering (95%), Data Structures & Algorithms (83%), Data Mining & Business Intelligence (89%)

I'm passionate about building market data pipelines and trading tools that traders and quants actually use every day, and now adding AI Agent intelligence to make those tools even more powerful.

Ideal Opportunities:

• Quant Developer / Trading Tools Engineer at hedge funds, asset managers, prop trading firms
• AI Agent / AI Infrastructure roles in fintech or trading technology
• Trading Technology teams building next-generation quant platforms
• AI × Trading startups building intelligent trading infrastructure

Why I'm Making This Transition:

• 3+ years building practical tools traders depend on daily
• Deep understanding of what traders actually need vs. what engineers think they need
• Now adding AI Agent + LLM engineering to amplify the impact of those tools
• Trading desks are moving toward AI, I want to lead that transformation

Coming soon on LinkedIn & Medium:

• How Multi-Agent Systems Transform QuantOps Workflows
• Trading Data Quality: The Agent-Powered Approach
• LangGraph vs Microsoft Agent Framework: A Real-World Comparison
• Drift Detection at Scale: Automated Monitoring with Agents

Rosalyn Chen | London, UK Quant Developer × AI Agent Engineer

Status: 🟢 Open to new opportunities

GitHub • LinkedIn • Email