Team Lead for Sophis Core Development at Leonteq. Working on trading platforms and learning about machine learning and AI.
I lead the Sophis Core Development team (4 engineers) and coordinate day-to-day changes, releases and incident response with colleagues in Zürich, Singapore and other offices. My work sits close to trading and risk operations, with a focus on reliability, performance and clear communication.
Over the past decade I’ve worked across pricing/analytics, reporting and automation, helped migrate Sophis from 6.3 to Fusion 7, and introduced monitoring with Grafana/Kibana. I enjoy simplifying workflows and working with teams operating 24/7 systems.
In parallel I’m building ML/AI skills through structured study (Machine Learning Specialization, Mathematics for ML; currently Deep Learning) and small applied projects (e.g., TradingLab ML integration, SQL MCP server, EtherFlow). My goal is to use ML where it adds value, with careful evaluation, observability and incremental rollout.
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.
Continuous learning initiatives alongside professional responsibilities
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.
Developing mobile-accessible Claude Code setup to increase productivity and efficiency. Building home workstation with remote access, exploring Flutter xterm integration, and optimizing persistent AI development sessions.
Building mobile agent coding setupBachelor'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 leading technical university. Built strong foundation in linear algebra, calculus, probability theory, and mathematical analysis. Transitioned to applied computer science to focus on practical implementation of mathematical concepts.
Currently progressing through this advanced specialization covering neural networks, deep learning, structuring ML projects, CNNs, and sequence models. Building advanced AI capabilities (20% completed).
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.
Personal development projects and applied experiments
A market analysis and trading strategy platform built with Blazor. Features technical analysis, Monte Carlo simulations and parameter optimization. Currently adding machine learning to train on backtesting results.
Ethereum transaction analysis tool for visualizing ETH transfer networks. Includes multiple visualization modes and analytics features such as pattern analysis, gas insights, and basic anomaly detection. Built with React.js, D3.js, and Alchemy SDK as a learning project.
Technical AI tools and infrastructure development
A real-time asteroid tracking and impact visualization application using machine learning. Built with Next.js, Three.js, and TensorFlow.js with interactive 3D visualizations and risk estimates.
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.
An offline Markdown reader built with Flutter to review markdown files on mobile. Available via Google Play Console internal test.
Experimental applications for exploration and learning
Interactive learning game prototype designed for children aged 6–7 years starting first grade.
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.
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
Experience working with business-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 interested in the intersection of technology, finance, and AI. I continue learning and exploring these areas.
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.
CV available on request.