Daniel Huber, Team & Tech Lead, Financial Technology @ Leonteq · Zürich
Architecting enterprise AI platforms in fintech, from infrastructure to production agents.
Focus: Agent Engineering · RAG · LLM Serving · AI Observability · AI Security
Stack: Python · LangChain · vLLM · C# · 10+ yrs
# AI systems journal, topics auto-collected by agent, deployed via GitHub Actions
Architecting and operating the internal AI platform at Leonteq that enables teams to build secure, observable, production-grade AI agents — from RAG and vLLM serving to observability. Team & Tech Lead with 10+ years in financial technology, currently leading three parallel initiatives: the Sophis core platform team, client onboarding, and the AI Foundry.
Team & Tech Lead at Leonteq, balancing three leadership roles: core platform team lead, tech lead on client onboarding, and tech lead on the AI Foundry initiative, driving internal AI adoption from infrastructure to agents.
Over the past decade I've progressed from analyst to director, working across pricing/analytics, regulatory projects (MiFID II, SFTR, FRTB), and platform migrations. Now focused on bridging the gap between traditional fintech and AI-powered workflows.
Took on tech lead responsibilities for two cross-team projects while continuing to lead the Sophis core development team.
Leading a team of 4 developers across Zürich and Lisbon. Handling incident resolution and operational risk analysis across time zones, coordinating with the Singapore and Amsterdam offices.
Implemented generic booking model for mirror booking, reducing client onboarding complexity. Built Grafana/Kibana monitoring. Key contributor to 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.
Personal projects applying ML/AI and agent development to financial technology
An AI systems journal where topics and news are automatically collected by an agent, pushed to the repo via GitHub Actions, and trigger a fresh deployment, no manual writing involved. The interesting part is the architecture: agent → content extraction → commit → CI/CD → live site.
A self-improving AI trading system inspired by Karpathy's autoresearch: 8 AI agents compete on real Kraken Futures crypto perpetuals while the system improves them overnight. It mutates agent configs, tests variants on past data, and keeps the winners. Agents reflect on mistakes and build rules to avoid them. When problems persist, the system writes its own code fixes. The question: will agents that learn from their mistakes actually outperform simpler ones?
A live near-Earth asteroid explorer built around two AI systems: a tool-calling chat agent that drives a 3D solar-system scene, selecting asteroids, changing views, and running impact simulations, and an autonomous monitoring agent on a 4-hour cron using an advisor pattern, a low-cost Haiku executor that escalates higher-stakes calls to an Opus advisor, with persistent memory and email alerts. Data from NASA's NEO API.
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.
Agent & RAG platforms are the area I focus on most. To show how I approach these systems end to end, here's a generalized reference architecture I put together.
A vendor-neutral enterprise RAG & agent platform. Pick a request flow to trace it through the stack.
Generic reference design, shown for illustration, not a depiction of any specific production system.
Bachelor'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 ETH Zürich. Built a foundation in linear algebra, calculus, probability theory, and mathematical analysis. Transitioned to applied computer science to focus on practical implementation of mathematical concepts.
Neural networks, CNNs, RNNs, sequence models, and deep learning optimization techniques.
View Certificate →Agent architectures, tool integration, Google ADK. Built a trading strategy agent for the Kaggle competition.
View Badge →Supervised/unsupervised learning, neural networks, deep learning fundamentals.
View Certificate →Linear algebra, calculus, probability, statistics.
View Certificate →Investment strategies, risk management, complex financial instruments.
View Certificate →Open to conversations about AI systems, fintech engineering, or agent architecture.