Flutter Experts

• Ride-sharing and bus platform, live and fully functional. Backend Spring Boot 4.1 on Java 21, PostgreSQL with Flyway, Testcontainers integration tests. Operation in my own AWS account (ECS Fargate, ALB, ECR, IAM least privilege) with CI/CD via GitHub Actions and OIDC federation without static credentials. Development throughout AI-assisted with Claude Code, including my own skills and project-specific memory. Spring Boot · Java 21 · PostgreSQL · Flyway · Docker · AWS ECS/ALB/ECR · CI/CD · GitHub Actions · Claude Code

• A digital system made up of special software and hardware components. The overall system replaces the traditional process with job slips and handwritten notes and enables more efficient order intake. Orders and work steps for the violin-making company’s projects can now be recorded, processed and logged in real time directly on the workshop’s touchscreen PC, by mobile phone or on the desktop. This gives customers a more transparent view of the work on their instruments and allows them to track the status and progress of their instrument through their customer account.

Tech stack: next.js, React, Flutter, Dart, Raspberry, Linux, Directus

AI Platform Backend – Senior Developer

Brought in to design and build a backend for an AI platform from the ground up, including multi-provider LLM orchestration and real-time infrastructure for AI influencer personas at scale.

Tasks and responsibilities

• Architecture and implementation of a multi-LLM orchestration layer with Semantic Kernel to integrate GPT-4 and other providers for core platform logic and AI influencer personas, reducing model-switching overhead by abstracting provider APIs behind a unified interface.
• Design and development of a backend from scratch in C# / .NET 10, including domain modeling with DDD, a versioned RESTful API layer, and cloud infrastructure setup on Azure.
• Building a real-time chat infrastructure with Server-Sent Events (SSE), message persistence, and delivery guarantees for the live operation of AI influencer personas at scale.
• Development of a media management service with integration of cloud object storage for upload and retrieval of influencer-generated content.
• Creation of an integration and unit test suite with data seeding for reliable regression testing across all core platform flows, significantly reducing production error rates.

Tools and technologies: C#, .NET, ASP.NET Core, Python, TypeScript, MySQL, Semantic Kernel, EF Core, Minimal APIs, LLM Orchestration, Prompt Engineering, Agentic AI, Generative AI, AI-Assisted Engineering, Claude Code, GitHub Copilot, Google Gemini, OpenAI API, Ollama, Redis, Azure, Azure Container Apps, Azure Database for MySQL, Docker, GitHub Actions, Clean Architecture, Vertical Slice Architecture, CQRS, Domain-Driven Design, REST API, xUnit, Integration Testing, Unit Testing, Jira, Confluence, Scrum

struppilog.com – Digital health record for pets / MVP → Full Product

Design, development, and full further development of a digital health platform for pets – from my own MVP development to a fully built and production-ready platform.

Independent concept and development of the MVP Development of the full application with Flutter/Dart and Firebase Expansion of the MVP into a full digital health record with health data, findings, allergies, medications, documents, and emergency data Development of user registration, authentication, roles, data models, and secure user interactions Implementation of QR-code-based data exchange and digital interaction features Development of a multilingual, responsive web application Integration of AI-supported features and AI/agentic workflows Development and continuous improvement of product logic, UX/UI, and technical architecture Building and expanding a scalable cloud-based solution with Firebase Integration and further development of APIs and external services Use of AI-native / agentic engineering to speed up development, testing, debugging, and product iteration Independent implementation of all other features and technical extensions Continuous further development of the MVP into a full digital product

Impact: The MVP I built myself was continuously developed technically and functionally into a broad, production-ready platform – including frontend, backend, data model, authentication, UX/UI, APIs, cloud infrastructure, and ongoing product development.

• Leading AI development for aquaculture startup, optimising shellfish visual assessments with machine learning and computer vision.
• Development and systematic evaluation of ML/CV algorithms for shellfish condition and morphometrics, using Python, Pytorch and MLFlow.
• Analysis of model performance, including identification of failure modes and edge cases in production deployments.
• Design of annotation strategies and refinement of labelled datasets for computer vision tasks.
• Detailed analysis of system performance and communication of findings through publication-quality technical reports to investors and fellow R&D staff.
• Responsible for delivery of technical roadmap.

• Remodeling of the system architecture for an Azure-based platform aimed at rapid development of new functionalities
• Modeling of a staging concept for the fulfillment of diverse customer and QA needs
• Creation of requirements for, and oversight of, a proof-of-concept supplier project for a Flutter app with highly advanced BLE functionalities
• Comparison of multiple observability platforms for feasibility and requirements fit within the project environment
• Creation of a mobile app architecture based on domain-driven architecture
• Position of technical advisor and accountable solution architect for two development teams
• Execution of architecture reviews and alignment of changes with architectural expectations
• Skills & Technologies: Microsoft Azure , App Services, NodeJS Mono-repository, microservice architecture, domain driven design, self contained systems, requirements engineering, CI/CD, DevOps, API design, solution architecture

• Optimization of the transmission media of the data links in the Ruhr area
• Configuration/expansion of switches and routers for an autonomous system AS
• Improve the network protocols used Layer 2/3 (OSI model), RSTP, broadcast, etc.
• Laying cables, crimping, antennas, routers, switches
• Maintenance, expansion, selection and prioritization of troubleshooting measures for network monitoring (PRTG, The Dude)
• Organization and new connection of another BGP peering, data center, NGN fiber optic
• VPN IPSec site connections for 6 sites with PFSense, Fritzbox
• Configuration of WLAN devices (Mikrotik, UBNT

Label: TCP/IP, VLAN, BGP, OSPF, MPLS, VPLS, routing protocols, WLAN, EoIP, PPPoE, routing, SNMP, DHCP, DNS, NAT, native IP, VLAN, Ethernet, VoIP., switches and routers from: Nortel, Cisco, MikroTik, UBNT., network management systems: The Dude, PRTG, UISP

• Enterprise-grade multi-agent AI orchestration platform built with Go and Kubernetes for scalable, secure agent coordination in cloud-native environments.
• Multi-agent orchestration with intelligent workload distribution and dynamic scaling.
• Cloud-native architecture with Kubernetes deployment and horizontal auto-scaling.
• Real-time coordination with sub-100ms agent communication using Go channels.
• Enterprise security with zero-trust architecture, RBAC, and comprehensive audit trails.
• Visual workflow engine with monitoring, observability, and API gateway integration.
• Technologies: Go, Kubernetes, ArangoDB, gRPC, Prometheus, Grafana.

• Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
• Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.

trategic Requirements Management: Steered user-centric product development for highly complex analytical platforms in close coordination with Product Owners.

Team Enablement: Provided technical supervision and UX alignment across multiple development teams to ensure consistent implementation.

Translation of Requirements: Translated complex business logic into intuitive, user-centric concepts and workflows along the entire customer journey.

End-to-End Design: Led ideation, conceptualization, and UI design for UX projects, accounting for cross-functional requirements.

Quality Assurance: Ensured strict adherence to and verification of usability standards and web accessibility (WCAG) within a B2B context.

Stakeholder Management: Confidently presented and defended design solutions to senior management and specialized departments.

Cross-functional Collaboration: Acted as the primary interface between Product, Engineering, and Business to resolve conflicting objectives within the design process.

• Context: Healthcare and laboratory teams required faster document analysis, treatment-planning support, and reliable AI workflows for MR/VR-assisted operations.
• Contribution: Built the AI healthcare platform, model/agent workflows, VR-glasses deployment platform, REST APIs, Next.js/React interfaces, and CI/CD pipelines.
• Impact: Delivered a production-ready AI product foundation that improved clinical document review, supported laboratory automation, and made VR fleet deployment manageable across environments.

Tech: TypeScript, Next.js, Node.js, React, Java, Spring Boot, Python, PyTorch, TensorFlow, Docker, PostgreSQL, OpenAPI, GitLab, GitHub Actions.

• Embedded software development for EMS safety boards in medical technology according to IEC 62304 / ISO 13485
• Technologies: C++, OpenCV, Python, Qt6, JTAG, UART, CMake, IEC 62304, ISO 13485

• Migrated the Billomat API integration to InvoiceNinja API, boosting system reliability by 33% for 1300 users.
• Established an ESLint configuration with Husky and lint-staged, resulting in a 23% decrease in code review issues and refined code consistency across 5 repositories.
• Improved team processes by refining the Kanban workflow.
• Optimized frontend performance, built a new CI/CD pipeline, and implemented AWS resource monitoring to strengthen deployment reliability and system observability.

• Analysis
• Consulting
• Software design
• Development
• Automation
• Testing
• Deployment
• Architecture, development and deployment of various proof-of-concept applications around the integration of current AI interfaces including conversational, realtime voice, images and videos
• Developed best practices for working with agentic systems and AI in practice
• Created code templates