Software development portfolio focused on full-stack applications, backend systems, desktop workflows, industrial process simulations, and applied computer vision.
I build projects with a pragmatic engineering focus: clear architecture, reproducible local setup, automated testing, CI, readable documentation, and realistic application workflows.
I hold Bachelor's and Master's degrees in Electrical Engineering and have a professional background in automation and industrial engineering.
I currently work as a full-stack developer for a pharmaceutical company, focusing on practical software systems, maintainable application structures, API integration, and workflow-oriented development.
My portfolio combines this engineering background with software development across full-stack applications, backend APIs, desktop systems, industrial workflows, financial analytics, and machine-learning model development.
- Full-stack development with React, TypeScript, ASP.NET Core, and REST APIs
- Backend development with .NET, Entity Framework Core, SQLite, Oracle, and ADO.NET
- Desktop application development with WPF and MVVM
- Applied computer vision with Python, PyTorch, TorchVision, and ONNX
- Anomaly detection, pretrained feature extraction, nearest-neighbor scoring, and model evaluation
- Python tooling, CLI development, packaging, artifact export, and automated quality checks
- Authentication, financial analytics, pharmaceutical workflows, industrial simulations, production tracking, and package-processing workflows
- CI/CD, automated testing, release preparation, repository hygiene, and technical documentation
- Cross-runtime model integration with ASP.NET Core, FastAPI, validated HTTP contracts, and dependency-aware health checks
| Project | Stack | Focus |
|---|---|---|
| Industrial Visual Anomaly Detection Model | Python, PyTorch, TorchVision, FastAPI, ONNX | PatchCore-inspired anomaly detection, pretrained ResNet18 features, feature-memory scoring, heatmaps, artifact export, and inference service |
| Industrial Visual Anomaly Detection Backend | ASP.NET Core, .NET 10, FastAPI, OpenAPI, xUnit | Versioned image-analysis API, secure upload validation, Python inference orchestration, readiness checks, Problem Details, tests, and CI |
| Portfolio Analytics Frontend | React, TypeScript, Vite | Authenticated portfolio UI, company discovery, trading workflows, charts, and analytics panels |
| Portfolio Analytics Backend | ASP.NET Core, EF Core, SQLite | REST API, authentication, demo-data seeding, and financial-data handling |
| Industrial Labeling Process Simulator | C#, .NET 10, xUnit, Mermaid | State-based labeling workflow, simulated devices, barcode validation, confirmation handling, and WAGO/Modbus boundary |
| Package Conveyor Workflow | C#, .NET 10, WPF, MVVM, SQLite | Package buffering, conveyor processing, catalog persistence, priority shipments, CSV workflows, and activity logging |
| Batch Release Workflow Backend | ASP.NET Core, Oracle, EF Core | Batch lifecycle API, workflow-state handling, and integration tests |
| Batch Release Workflow Desktop | C#, WPF, MVVM | Desktop workflow UI, batch checks, audit trails, and Excel export |
| EMVS Simulation Lab | C#, WPF, MVVM, Oracle | Desktop simulation for pharmaceutical verification workflows with JSON and optional Oracle persistence |
| Production Order Tracking System | C#, .NET 10, Oracle ADO.NET, xUnit | State-based production tracking, sequential processing steps, event history, and interchangeable persistence |
| Python HTTP Client Demo | Python, httpx, pytest | CLI and library package, retry handling, mocked tests, linting, and security checks |
- Languages: C#, Python, TypeScript, JavaScript, SQL
- Applied AI and Computer Vision: PyTorch, TorchVision, ONNX, ONNX Runtime, Pillow, NumPy
- Frontend: React, Vite, HTML, CSS
- Backend: ASP.NET Core, FastAPI, Entity Framework Core, ADO.NET, REST APIs
- Databases: SQLite, Oracle
- Desktop: WPF, MVVM
- Testing and Quality: unittest, pytest, xUnit, ESLint, Prettier, Black, isort, Flake8, Bandit
- Tooling: GitHub Actions, Swagger/OpenAPI, Git, npm, pip, dotnet CLI
- Architecture and Workflows: dependency injection, MVVM, state machines, ports and adapters, deterministic data pipelines
Across my projects, I focus on:
- separating domain, infrastructure, presentation, and model-development concerns
- building reproducible workflows instead of one-off demonstrations
- validating assumptions with tests, metrics, and documented evidence
- keeping secrets, datasets, generated artifacts, and machine-specific configuration out of source control
- documenting limitations and distinguishing experimental results from production readiness
- designing application boundaries that can support web, desktop, and backend consumers
The repositories are prepared as portfolio projects with:
- clear README documentation
- reproducible local setup instructions
- automated CI workflows where appropriate
- focused unit and integration tests
- screenshots for visual and workflow-oriented projects
- release notes and versioned milestones
- validation commands and documented technical decisions
- backend-only handling of secrets and provider keys
The industrial visual anomaly-detection system is experimental and educational. It is not a certified production inspection system. MVTec datasets and generated dataset-derived artifacts are not distributed through the repository.
- GitHub: rluetken-dev