I combine 10+ years of financial technology experience with emerging ML/AI expertise. Currently developing deep learning capabilities while leading a development team in mission-critical financial systems.
As a Team Lead at Leonteq Securities AG, I lead a development team while coordinating daily operations across offices in Zürich, Singapore, Hong Kong, Amsterdam, Guernsey, and Lisbon. Over 10+ years, I've architected mission-critical financial systems—from distributed platforms processing 70 parallel workflows to serving as the global 24/7 support escalation point.
Alongside my leadership role, I'm deeply committed to the AI/ML transformation, investing 15-20 hours weekly in structured learning. I've completed Stanford's Machine Learning Specialization and DeepLearning.AI's Mathematics for ML, and I'm actively building applications that merge my financial expertise with cutting-edge ML capabilities.
My unique profile combines enterprise-scale leadership experience with hands-on ML/AI development skills. I understand what it takes to run mission-critical systems in production—the technical depth, operational excellence, and global coordination required. This positions me well to help organizations deploy AI solutions that are not just innovative, but reliable and scalable.
Leading a team of 4 developers while serving as the primary interface between technical team and business stakeholders. Managing incident investigations, estimating requirements for business analysts, and coordinating with operational risk teams. Acting as a shield for the development team by handling stakeholder requests and operational disruptions.
Enhanced system monitoring using Grafana and Kibana for real-time analytics. Developed intraday PNL/PLA reporting systems for trading desk. Led critical projects including LIBOR migration.
Progressed through multiple roles focusing on automation, risk management, and regulatory compliance.
Focused on reporting automation and distributed system implementation. Automated creation of listed instruments via SmartCo integration.
Personal development projects pursued in my free time alongside my leadership role (15-20 hours/week)
Personal project combining my financial domain expertise with newly acquired ML skills. Implementing machine learning algorithms to train on backtesting trade data for strategy optimization.
Phase 3 of 4: ML Training Implementation | Personal ProjectProgressing through DeepLearning.AI courses on neural networks, CNNs, and sequence models.
Built mcp-ms-sql-server for database integration. Developing AI workflow automation tools and exploring Claude Code integration for financial workflows.
Exploring AI integration in enterprise workflowsBachelor's degree in Computer Science with focus on practical software engineering, algorithms, and system design. Completed several industry-relevant projects including algorithmic trading, GPU programming, and financial portfolio management systems.
Mathematics studies at Switzerland's premier technical university, building strong analytical and problem-solving foundations that continue to benefit my technical work.
Currently enrolled in this advanced course covering neural networks, deep learning, structuring ML projects, CNNs, and sequence models. Building advanced AI capabilities.
Completed comprehensive mathematics program covering linear algebra, calculus, probability, and statistics essential for ML and data science.
Completed comprehensive machine learning program covering supervised learning, unsupervised learning, neural networks, and deep learning fundamentals.
Advanced program focusing on structured products investment strategies, risk management, and market dynamics in complex financial instruments.
Self-directed projects combining financial domain expertise with ML/AI capabilities
A comprehensive market analysis and trading strategy platform built with Blazor. Features technical analysis, Monte Carlo simulations, parameter optimization, and AI-powered analysis. Currently implementing machine learning algorithms to train on generated trade data from backtesting results.
A configurable Model Context Protocol (MCP) server for Microsoft SQL Server integration with Claude Code and other MCP clients. Enables AI assistants to securely interact with SQL Server databases through project-based configurations with full read/write capabilities.
Comprehensive Ethereum transaction analysis platform for visualizing and understanding ETH transfer networks. Features multiple visualization modes (network graphs, timeline view, volume heatmaps, tree maps), advanced analytics (pattern analysis, gas optimization, profit/loss tracking), anomaly detection, and wallet behavior profiling. Includes search management, data export capabilities, and risk assessment tools. Built with React.js, D3.js, and Alchemy SDK as an AI-assisted development learning project with Claude Code.
Interactive simulation of James Lovelock's Daisyworld model, demonstrating planetary self-regulation through feedback mechanisms between life (white/black daisies) and temperature. Enhanced implementation of a complex systems investigation originally explored in high school.
An experimental SEO project exploring LLM optimization through a fictional quantum tea brewing concept. Implements Vercel's AI SEO strategies including structured data, semantic HTML, clear information architecture, and machine-consumable APIs. Built to test how AI search engines discover and parse content.
Interactive learning game application built with generative AI technologies. Educational platform designed for children aged 6-7 years who are entering first grade, providing engaging gameplay to support their learning journey.
Study project combining textual data mining with technical analysis and portfolio theory for automated trading strategies.
GPU-accelerated image processing using wavelet transformation and parallel algorithms for progressive graphics file format optimization.
Portfolio management and trend analysis system developed for Dufour Capital AG, combining financial mathematics with web technologies.
Self-taught through 500+ hours of structured learning and hands-on projects
Deep expertise in mission-critical financial systems
Currently enrolled in comprehensive ML courses, building practical knowledge through hands-on projects and experimentation.
Developing interest in understanding how neural networks work internally. Planning to gain practical knowledge in this area by end of 2025.
Actively experimenting with Claude Code and MCP, building practical applications and understanding AI integration patterns.
Beyond my professional work, I'm passionate about the intersection of technology, finance, and AI. My personal interests drive continuous learning and exploration in cutting-edge fields.
Deep diving into ML algorithms, neural networks, and their practical applications in finance and trading.
Building practical applications with generative AI, exploring Claude Code, MCP, and AI-powered tools.
Passionate about financial markets, derivatives, structured products, and innovative financial technologies.
Developing and optimizing trading strategies using technical analysis, Monte Carlo simulations, and AI.
Exploring blockchain technologies, smart contracts, and their applications in modern financial systems.
Feel free to reach out for professional discussions about technology, machine learning, or financial systems.