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 Stanford's ML Specialization, Mathematics for ML, and Google's 5-day agent course. Built backtest-agent with Google ADK for a Kaggle competition. Currently applying these agent skills (LangChain) to trading analysis projects. 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, built agent for Kaggle competition
Deep expertise in mission-critical financial systems
Applying agent development to financial systems - 15-20 hours/week
Applying agent concepts to trading analysis and risk management through projects like backtest-agent. Combining 10+ years FinTech domain knowledge with agent development skills. Building practical agent-based solutions for financial systems and trading strategies.
Completed Google's 5-day intensive agent course and LangGraph training. Built backtest-agent using Google ADK for Kaggle competition. Learned tool integration, state management, and multi-agent architectures.
Completed Stanford Machine Learning Specialization and Mathematics for ML. Built solid understanding of supervised/unsupervised learning, neural networks, and mathematical foundations.
Personal projects demonstrating ML/AI learning and financial technology expertise
Trading strategy backtesting agent developed for Google's 5-day intensive agent course Kaggle competition. Built using agent frameworks to automate trading strategy analysis and optimization. Practical application of LangGraph and agent development concepts.
Ethereum transaction analysis platform for visualizing ETH transfer networks. Features multiple visualization modes, advanced analytics, anomaly detection, and wallet behavior profiling. Built with React.js, D3.js, and Alchemy SDK.
Real-time asteroid tracking and impact visualization application powered by machine learning. Built with Next.js, Three.js, and TensorFlow.js, providing near-Earth object monitoring with interactive 3D visualizations and AI-driven risk predictions.
Personal project combining FinTech domain knowledge with ML learning. Built with Blazor, features technical analysis, Monte Carlo simulations and backtesting. Exploring how to apply agent-based approaches to strategy optimization.