About me

🎯 Summary

I design robust software systems under real-world constraints.

With more than 20 years of experience, I help organizations translate complex requirements into stable, maintainable, and economically sound solutions – with or without AI.

My focus is not on maximum automation, but on making the right technical decisions:
When AI creates real value – and when classical or hybrid approaches are more robust, cost-effective, and reliable.

🛠️ Technical Expertise

Core Skills (20+ Years Experience)

“For me, technologies are a means to an end. It is not the tool that is decisive, but its suitability under technical, legal and economic constraints.”

Programming Languages:

• Primary: Java, Python, JavaScript/TypeScript
• Secondary: Kotlin, C/C++, C#, Solidity

Software Architecture:

• Microservices, Event-Driven Architecture, Domain-Driven Design
• REST/SOAP APIs, Message-Driven Integration (Kafka, RabbitMQ)
• Cloud-Native Applications (12-Factor App Principles)
• Distributed Systems, Scalability Patterns

Frameworks & Technologies:

• Backend: Spring Boot, FastAPI, Node.js
• Frontend: Angular, React, Vue.js
• Databases: PostgreSQL, Oracle, MongoDB, Vector Databases
• DevOps: Docker, Kubernetes, Helm, Jenkins, GitLab CI/CD

Cloud & Infrastructure:

• AWS (EC2, S3, Lambda, SageMaker, ECS)
• OpenShift, Cloud Foundry, Kubernetes
• Infrastructure as Code (Terraform, CloudFormation)

🤖 AI/ML as Strategic Tool

When I Use AI:

• Natural Language Processing Tasks (Classification, Extraction, Generation)
• Computer Vision (Object Detection, Document Analysis)
• Complex Decision Processes Where Rule-Based Systems Don’t Scale
• Automation with Genuine ML Value-Add

When I Advise AGAINST AI:

• Deterministic Business Logic (Auditability More Important Than Flexibility)
• Small Datasets (Rule-Based More Efficient)
• Critical Systems Without Error Tolerance
• When Maintainability > Accuracy

Practical AI Skills:

• LLM Integration: RAG Systems, Prompt Engineering, Model Selection
• Agentic AI: Multi-Agent Orchestration (LangGraph), Tool Integration
• MLOps: Model Training Pipelines, A/B Testing, Monitoring, Versioning
• Frameworks: PyTorch, TensorFlow, LangChain, Hugging Face
• Deep Learning: CNN (Computer Vision), Transformer (NLP), RL (Robotics)

“I don’t see AI as a replacement for engineering, but rather as an amplifier – when used correctly, clearly defined and professionally accountable.”

🎯 Working Approach & Methods

Development Processes:

• Agile/Scrum, Test-Driven Development (TDD)
• Clean Code, Design Patterns (GoF, Enterprise Patterns)
• Continuous Integration/Continuous Deployment (CI/CD)
• Code Reviews

Architecture Approach:

1. Understand Business Problem (Not Just Tech Requirements)
2. Evaluate Constraints (Budget, Timeline, Team, Infrastructure)
3. Assess Technology Options (AI, Traditional, Hybrid)
4. Design for Maintainability and Scalability
5. Build Production-Ready (Monitoring, Error Handling, Security)
6. Decisions with a view to responsibility, maintainability and long-term risks
7. Conscious reduction of complexity instead of maximum automation
8. Designing systems so that they remain manageable even under uncertainty

🎓 Certifications & Education
“The following further training courses are not an end in themselves, but rather serve to strengthen my ability to realistically evaluate, limit and responsibly utilise AI.”