Team Lead for Sophis Core Development at Leonteq. Learning to build AI agents with LangChain and LangGraph, combining ML foundations with agent architectures.
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.
Outside work, I've 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.
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.
Applying Agent Development to Financial Systems
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.
Current focus: Building agent-based financial applicationsCompleted 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: Courses + Kaggle competition submissionCompleted Stanford ML Specialization and Mathematics for ML. Built solid understanding of supervised/unsupervised learning, neural networks, and mathematical foundations.
Completed: 500+ hours investedBachelor'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 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 learned in recent training.
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.
Built while learning about ML and 3D visualization. Uses NASA API, TensorFlow.js for basic predictions, and Three.js for interactive visualizations. A fun project to explore ML concepts outside finance.
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.
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.
500+ hours invested in ML/AI foundations, built agent for Kaggle competition
Experience working with business-critical financial systems
Completed Stanford Machine Learning Specialization and Mathematics for ML. Built understanding of supervised/unsupervised learning, neural networks, and mathematical foundations.
Completed LangGraph courses. Learning about agent architectures, tool integration, and state management. Exploring how to apply these concepts to financial systems.
Seeking opportunities to apply agent development in enterprise FinTech. Continuing Deep Learning Specialization and building practical agent-based projects.
Beyond my professional work, I'm interested in the intersection of technology, finance, and AI. I continue learning and exploring these areas.
Completed foundational ML training (Stanford/DeepLearning.AI). Interested in applying ML techniques to trading strategies, risk analysis, and financial forecasting.
Learning to build autonomous agents with LangChain and LangGraph. Interested in applying agent-based systems to financial analysis, risk management, and trading automation.
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.