Experienced Team Lead with 10+ years in financial technology. Currently leading development team in mission-critical trading systems at Leonteq while pursuing ML/AI learning. Completed 500+ hours of training (Stanford ML, Mathematics for ML, LangGraph courses). Exploring how to apply agent-based systems to FinTech automation. Proven track record in stakeholder management, incident investigation, and team leadership.
Leading a team of 4 developers while serving as primary interface between technical team and business stakeholders. Managing incident investigations, requirements estimation for Business Analysts, and coordinating with operational risk teams.
Enhanced system monitoring using Grafana and Kibana for real-time analytics. Developed intraday PNL/PLA reporting systems for trading desk. Key contributor to 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.
Bachelor's degree in Computer Science with focus on practical software engineering, algorithms, and system design. Completed 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.
Advanced course covering neural networks, deep learning, structuring ML projects, CNNs, and sequence models. Building advanced AI capabilities.
Comprehensive mathematics program covering linear algebra, calculus, probability, and statistics essential for ML and data science.
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.
500+ hours invested in ML/AI, exploring agents
Deep expertise in mission-critical financial systems
Investing 15-20 hours/week in ML/AI learning, recently pivoting to agent development
Completed Stanford Machine Learning Specialization and Mathematics for ML. Built solid understanding of supervised/unsupervised learning, neural networks, and mathematical foundations.
Recently completed LangGraph courses (late 2024/early 2025). Learning about agent architectures, tool integration, state management, and LangChain orchestration patterns.
Exploring how to apply agent concepts to trading analysis, risk management, and financial automation. Working on personal projects to combine 10+ years FinTech domain knowledge with new agent skills.
Recent personal development projects showcasing ML/AI integration and financial technology expertise
Comprehensive Ethereum transaction analysis platform for visualizing and understanding ETH transfer networks. Features multiple visualization modes, advanced analytics, anomaly detection, and wallet behavior profiling. Built with React.js, D3.js, and Alchemy SDK.
Sophisticated real-time asteroid tracking and impact visualization application powered by machine learning. Built with Next.js, Three.js, and TensorFlow.js, providing comprehensive near-Earth object monitoring with interactive 3D visualizations and AI-driven risk predictions.
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.