Guten Tag
Dr. Florian Drechsler
Mathematician | Software Engineer | AI Developer
Combining mathematical rigor with modern software engineering. Currently developing intelligent systems for predictive maintenance and energy optimization at TreeTek, while building an AI Development Framework to revolutionize software development workflows.
About
With a PhD in Mathematics (Dr. rer. nat.) from the Max Planck Institute for Mathematics in the Sciences, a Diploma in Mathematics, and a BSc in Computer Science from Universität Leipzig, I combine deep theoretical knowledge with practical engineering expertise.
Currently developing intelligent systems for predictive maintenance and energy network optimization at TreeTek, while building an AI Development Framework that revolutionizes how software is developed using AI-assisted workflows.
Academic Background
- • PhD in Mathematics (Dr. rer. nat.)
- • Diploma in Mathematics
- • BSc in Computer Science
Professional Experience
Over 5 years building production systems: DPI software at Ipoque, logistics intelligence at NTA Mexico, and currently ML-based predictive maintenance at TreeTek
Current Focus
Building intelligent maintenance prediction systems and energy network optimizers at TreeTek, while developing an AI Development Framework for automated software workflows
Experience
Software Engineer at TreeTek
Developing predictive maintenance systems and optimization algorithms for energy networks and central heating systems using machine learning and sensor data analysis.
Software Engineer at IT Sonix
Developed audio-based predictive maintenance for conveyor belts. Built microcontroller firmware in C for Azure IoT Hub integration, Spring backend for data storage, and Python ML models for audio analysis. Automated billing systems for renewable energy sector.
Software Engineer at Ipoque GmbH
Designed and implemented DPI software PACE2. Source code restructuring following Clean Code principles, performance optimizations, test-driven development in C and Python, and KPI definition.
Logistics Intelligence Coordinator at NTA Mexico
Cost analysis, pricing strategies, business process automation, and Business Intelligence system implementation for pharmaceutical logistics company.
Postdoctoral Researcher at Max Planck Institute
Advanced development of the H-LibPro software library for hierarchical matrices and numerical algorithms.
PhD in Mathematics (Dr. rer. nat.)
Max Planck Institute for Mathematics in the Sciences, Leipzig. Dissertation: Solving elliptic boundary value problems using domain decomposition, hierarchical matrices, and finite element methods. Grade: magna cum laude. Contributed to DFG project FEMINVERS for brain activity localization.
BSc Computer Science, Universität Leipzig
Bachelor of Science in Computer Science with minor in Mathematics. Thesis: Parallel reduction of rank-k matrices. Grade: 2.0
Diploma in Mathematics, Universität Leipzig
Diploma in Mathematics with minor in Computer Science. Thesis: Optimization of cluster trees for H-matrix structures. Grade: 1.0 (excellent)
Skills
Programming Languages
Frameworks & Technologies
Machine Learning & AI
Mathematics & Algorithms
Software Engineering Practices
Embedded & Systems
Interests
IT & Software Development
Building robust, scalable systems with modern technologies
AI & Machine Learning
Exploring the frontiers of artificial intelligence and its applications
Mathematics Research
Investigating theoretical foundations and their practical implications
Innovation
Finding creative solutions at the intersection of theory and practice
AI Development Framework
A comprehensive framework orchestrating multiple AI agents for automated software development. Features ticket-based workflows, specialized agents (implementation, test, review), automated documentation, and human oversight checkpoints. Successfully deployed in production with 41 tickets completed across 16 AI sessions.
How It Works
The framework uses a ticket-based workflow where tickets progress through defined statuses (Draft → Ready → Working → Review → Done). During the Working phase, specialized AI agents collaborate autonomously: the Implementation Agent writes code, the Test Agent creates and executes tests, and the Review Agent performs quality checks. All agents iterate until quality standards are met, with human approval required only at the final Review checkpoint before merging.
Key Benefits
- Automated Development: Agents handle implementation, testing, and review autonomously
- Quality Assurance: Built-in iteration loops ensure code quality before human review
- Documentation: Comprehensive ADRs, session logs, and deployment guides automatically generated
- Traceability: GitHub issues, labels, and comments provide complete audit trail
- Human Oversight: Critical decisions and final approval remain with human developers
Showcase Projects
Real-world projects built with the AI Development Framework, demonstrating its capabilities in production environments.
Frequently Asked Questions
Common questions about my background, expertise, and services.
Get In Touch
Interested in collaboration, have a project idea, or just want to connect? I'm always open to discussing new opportunities and interesting challenges.