Experienced Team Lead with 10+ years in financial technology at Leonteq, currently balancing three leadership roles: core platform team lead, tech lead on a client onboarding project (C#, REST API), and tech lead on the AI Foundry initiative. Completed Stanford's ML Specialization, Deep Learning Specialization, and Google's 5-day agent course. Now applying those skills hands-on - setting up internal AI infrastructure, stabilizing RAG systems, and advising teams building AI agents. Proven track record in stakeholder management, incident investigation, and team leadership.
Took on tech lead responsibilities for two cross-team projects alongside continued team lead duties for the Sophis core development team.
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 building analytical and problem-solving foundations.
Neural networks, CNNs, and sequence models.
Hands-on course covering AI agent architectures, tool integration, and multi-agent systems. View Badge
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.
Solid experience 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
Agentic learning experiment combining LangGraph and vector search for crypto trading analysis. Explores multi-agent architectures for market data retrieval and strategy evaluation.
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.
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.