Tobias Steidle Software Architect | Designing Solutions Under Real-World Constraints
\nSometimes AI, Always Engineering Judgment | 20+ Years<\/small><\/h2>\n<\/div>\n<\/div>\n\n\nAntoniusweg 14
\nD-86570 Inchenhofen<\/div>\n<\/i> M<\/abbr>: +49 175 29 31 082
\nE<\/abbr>: tobias.steidle@softwaredev.de<\/a>
\n<\/i><\/abbr>: https:\/\/www.linkedin.com\/in\/tobias-steidle\/<\/a><\/p>\nCurrent Profile (DOCX): Download<\/a>
\nCurrent Profile (PDF): Download<\/a><\/div>\n\n- <\/li>\n<\/ul>\n<\/div>\n
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\n<\/a><\/div>\n\n![\"\"](\"https:\/\/softwaredev.de\/wp-content\/uploads\/2019\/06\/profil.jpeg\")
\nTobias Steidle<\/p>\n<\/div>\n<\/div>\n
\n\n\n\n🎯 Summary<\/h3>\n
I design robust software systems under real-world constraints.<\/strong><\/p>\nWith more than 20 years of experience, I help organizations translate complex requirements into stable, maintainable, and economically sound solutions \u2013 with or without AI.<\/p>\n
My focus is not on maximum automation, but on making the right technical decisions<\/strong>:
When AI creates real value \u2013 and when classical or hybrid approaches are more robust, cost-effective, and reliable.<\/p>\n<\/pre>\n🛠️ Technical Expertise<\/h2>\nCore Skills (20+ Years Experience)<\/strong><\/h3>\n„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.“<\/em><\/p>\nProgramming Languages:<\/strong><\/p>\n\n- Primary:<\/strong> Java, Python, JavaScript\/TypeScript<\/li>\n
- Secondary:<\/strong> Kotlin, C\/C++, C#, Solidity<\/li>\n<\/ul>\n
Software Architecture:<\/strong><\/p>\n\n- Microservices, Event-Driven Architecture, Domain-Driven Design<\/li>\n
- REST\/SOAP APIs, Message-Driven Integration (Kafka, RabbitMQ)<\/li>\n
- Cloud-Native Applications (12-Factor App Principles)<\/li>\n
- Distributed Systems, Scalability Patterns<\/li>\n<\/ul>\n
Frameworks & Technologies:<\/strong><\/p>\n\n- Backend:<\/strong> Spring Boot, FastAPI, Node.js<\/li>\n
- Frontend:<\/strong> Angular, React, Vue.js<\/li>\n
- Databases:<\/strong> PostgreSQL, Oracle, MongoDB, Vector Databases<\/li>\n
- DevOps:<\/strong> Docker, Kubernetes, Helm, Jenkins, GitLab CI\/CD<\/li>\n<\/ul>\n
Cloud & Infrastructure:<\/strong><\/p>\n\n- AWS (EC2, S3, Lambda, SageMaker, ECS)<\/li>\n
- OpenShift, Cloud Foundry, Kubernetes<\/li>\n
- Infrastructure as Code (Terraform, CloudFormation)<\/li>\n<\/ul>\n
<\/pre>\n🤖 AI\/ML as Strategic Tool<\/strong><\/h3>\nWhen I Use AI:<\/strong><\/p>\n\n- Natural Language Processing Tasks (Classification, Extraction, Generation)<\/li>\n
- Computer Vision (Object Detection, Document Analysis)<\/li>\n
- Complex Decision Processes Where Rule-Based Systems Don’t Scale<\/li>\n
- Automation with Genuine ML Value-Add<\/li>\n<\/ul>\n
When I Advise AGAINST AI:<\/strong><\/p>\n\n- Deterministic Business Logic (Auditability More Important Than Flexibility)<\/li>\n
- Small Datasets (Rule-Based More Efficient)<\/li>\n
- Critical Systems Without Error Tolerance<\/li>\n
- When Maintainability > Accuracy<\/li>\n<\/ul>\n
Practical AI Skills:<\/strong><\/p>\n\n- LLM Integration:<\/strong> RAG Systems, Prompt Engineering, Model Selection<\/li>\n
- Agentic AI:<\/strong> Multi-Agent Orchestration (LangGraph), Tool Integration<\/li>\n
- MLOps:<\/strong> Model Training Pipelines, A\/B Testing, Monitoring, Versioning<\/li>\n
- Frameworks:<\/strong> PyTorch, TensorFlow, LangChain, Hugging Face<\/li>\n
- Deep Learning:<\/strong> CNN (Computer Vision), Transformer (NLP), RL (Robotics)<\/li>\n<\/ul>\n
„I don’t see AI as a replacement for engineering, but rather as an amplifier \u2013 when used correctly, clearly defined and professionally accountable.“<\/em><\/p>\n<\/pre>\n🎯 Working Approach & Methods<\/h2>\n
Development Processes:<\/strong><\/p>\n\n- Agile\/Scrum, Test-Driven Development (TDD)<\/li>\n
- Clean Code, Design Patterns (GoF, Enterprise Patterns)<\/li>\n
- Continuous Integration\/Continuous Deployment (CI\/CD)<\/li>\n
- Code Reviews<\/li>\n<\/ul>\n
Architecture Approach:<\/strong><\/p>\n\n- Understand Business Problem (Not Just Tech Requirements)<\/li>\n
- Evaluate Constraints (Budget, Timeline, Team, Infrastructure)<\/li>\n
- Assess Technology Options (AI, Traditional, Hybrid)<\/li>\n
- Design for Maintainability and Scalability<\/li>\n
- Build Production-Ready (Monitoring, Error Handling, Security)\n<\/li>\n<\/li>\n
- Decisions with a view to responsibility, maintainability and long-term risks<\/li>\n
- Conscious reduction of complexity instead of maximum automation<\/li>\n
- Designing systems so that they remain manageable even under uncertainty<\/li>\n<\/li>\n<\/ol>\n
<\/pre>\n🎓 Certifications & Education<\/span>
\n„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.“<\/em><\/p>\n<\/div>\n<\/div>\nSpecialization Deep Learning<\/strong><\/p>\n\n- Neural Networks and Deep Learning (Coursera) Download<\/a><\/li>\n
- Improving DNNs: Hyperparameter Tuning, Regularization and Optimization (Coursera) Download<\/a><\/li>\n
- Structuring Machine Learning Projects (Coursera) Download<\/a><\/li>\n
- Convolutional Neural Networks (Coursera) Download<\/a><\/li>\n
- Sequence Models (Coursera) Download<\/a><\/li>\n
- Deep Learning Specialization (Coursera) Download<\/a> \/ Badge<\/a><\/li>\n<\/ul>\n
Specialization Machine Learning Engineering for Production (MLOps)<\/strong><\/p>\n\n- Introduction to Machine Learning in Production (Coursera) Download<\/a><\/li>\n
- Machine Learning Data Lifecycle in Production (Coursera) Download<\/a><\/li>\n
- Machine Learning Modeling Pipelines in Production (Coursera) Download<\/a><\/li>\n
- Deploying Machine Learning Models in Production (Coursera) Download<\/a><\/li>\n
- Machine Learning Engineering for Production (MLOps) Specialization (Coursera) Download<\/a><\/li>\n<\/ul>\n
Specialization Mathematics for Machine Learning and Data Science<\/strong><\/p>\n\n- Linear Algebra for Machine Learning and Data Science (Coursera) Download<\/a><\/li>\n
- Calculus for Machine Learning and Data Science (Coursera) Download<\/a><\/li>\n
- Probability & Statistics for Machine Learning & Data Science (Coursera) Download<\/a><\/li>\n
- Mathematics for Machine Learning and Data Science Specialization Download<\/a><\/li>\n<\/ul>\n
AI \/ Machine Learning<\/strong><\/p>\n\n- Agentic AI (Nanodegree) Download<\/a><\/li>\n
- Generative AI with Large Language Models (Coursera) Download<\/a><\/li>\n
- Machine Learning Engineer Certificate (Nanodegree) Download<\/a><\/li>\n
- Deep Reinforcement Learning Certificate (Nanodegree) Download<\/a><\/li>\n
- Computer Vision Certificate (Nanodegree) Download<\/a><\/li>\n
- Natural Language Processing Certificate (Nanodegree) Download<\/a><\/li>\n<\/ul>\n
Robotics<\/strong><\/p>\n\n- Self-Driving Car Engineer Certificate (Nanodegree) Download<\/a><\/li>\n
- Robotic Software Engineer (Nanodegree) Download<\/a>
\n<\/strong><\/li>\n<\/ul>\nBlockchain<\/strong><\/p>\n\n- Blockchain Developer Certificate (Nanodegree) Download<\/a><\/li>\n<\/ul>\n<\/div>\n<\/div>\n
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\n\n💡 Availability & Preferences<\/h2>\n
Status:<\/strong> Currently in project until at least end of 2026, available for discussions about future projects<\/p>\nService Area:<\/strong> Augsburg \u2013 Ingolstadt \u2013 Munich (Remote preferred)<\/p>\nProject Types:<\/strong><\/p>\n
\nD-86570 Inchenhofen<\/div>\n
<\/i> M<\/abbr>: +49 175 29 31 082 I design robust software systems under real-world constraints.<\/strong><\/p>\n With more than 20 years of experience, I help organizations translate complex requirements into stable, maintainable, and economically sound solutions \u2013 with or without AI.<\/p>\n My focus is not on maximum automation, but on making the right technical decisions<\/strong>: „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.“<\/em><\/p>\n Programming Languages:<\/strong><\/p>\n Software Architecture:<\/strong><\/p>\n Frameworks & Technologies:<\/strong><\/p>\n Cloud & Infrastructure:<\/strong><\/p>\n When I Use AI:<\/strong><\/p>\n When I Advise AGAINST AI:<\/strong><\/p>\n Practical AI Skills:<\/strong><\/p>\n „I don’t see AI as a replacement for engineering, but rather as an amplifier \u2013 when used correctly, clearly defined and professionally accountable.“<\/em><\/p>\n Development Processes:<\/strong><\/p>\n Architecture Approach:<\/strong><\/p>\n 🎓 Certifications & Education<\/span> Specialization Deep Learning<\/strong><\/p>\n Specialization Machine Learning Engineering for Production (MLOps)<\/strong><\/p>\n Specialization Mathematics for Machine Learning and Data Science<\/strong><\/p>\n AI \/ Machine Learning<\/strong><\/p>\n Robotics<\/strong><\/p>\n Blockchain<\/strong><\/p>\n <\/p>\n Status:<\/strong> Currently in project until at least end of 2026, available for discussions about future projects<\/p>\n Service Area:<\/strong> Augsburg \u2013 Ingolstadt \u2013 Munich (Remote preferred)<\/p>\n Project Types:<\/strong><\/p>\n
\nE<\/abbr>: tobias.steidle@softwaredev.de<\/a>
\n<\/i><\/abbr>: https:\/\/www.linkedin.com\/in\/tobias-steidle\/<\/a><\/p>\n
\nCurrent Profile (PDF): Download<\/a><\/div>\n\n
\n<\/a><\/div>\n
\nTobias Steidle<\/p>\n<\/div>\n<\/div>\n🎯 Summary<\/h3>\n
When AI creates real value \u2013 and when classical or hybrid approaches are more robust, cost-effective, and reliable.<\/p>\n<\/pre>\n
🛠️ Technical Expertise<\/h2>\n
Core Skills (20+ Years Experience)<\/strong><\/h3>\n
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🤖 AI\/ML as Strategic Tool<\/strong><\/h3>\n
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🎯 Working Approach & Methods<\/h2>\n
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\n„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.“<\/em><\/p>\n<\/div>\n<\/div>\n\n
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💡 Availability & Preferences<\/h2>\n