Mobile Developers in Germany

• Hands-on development of AI-native product prototypes with specialized AI agents for research, requirements, business logic, UX/flow design, test case generation and quality assurance.
• Structured use and orchestration of AI agents through clearly defined roles, inputs/outputs and handover points; breaking down complex product tasks into verifiable work packages and iterative prototyping cycles.
• Establishment of human-in-the-loop quality gates to validate AI-generated results for functional correctness, consistency, completeness and feasibility; targeted rework cycles in case of deviations.
• Development of a regulatory GenAI/rules prototype for CRD VI with a structured decision flow, web UI, rule-based validation and automated test cases; iteration of the business logic through to a pilot-ready POC.
• Design of an AI-to-Action banking prototype: AI intent → consent → bank/product logic → conversion including admin console; translating the product idea into MVP scope, role model, user flows and clickable prototypes.

• Planned and developed a CPU monitoring application for monitoring system performance and resource utilization.
• Designed and implemented a Spring Boot backend providing a REST API for processing and exposing monitoring data.
• Developed the React frontend for presenting monitoring information in a clear and user-friendly interface.
• Integrated PostgreSQL for persistent storage and management of application data.
• Containerized the application and its services using Docker Compose.
• Automated infrastructure provisioning and deployment using Terraform on AWS.
• Structured the application as a modern, maintainable system using REST-based communication between frontend and backend.
• Designed and developed a secure, scalable CPU monitoring architecture (cpu-watcher) with a dedicated collector application that streams monitoring data to the backend, reducing direct exposure of system resources.
• Designed a secure cloud infrastructure with the database isolated within a private network and OIDC-based authentication.
• Implemented Infrastructure as Code with Terraform and integrated version-controlled CI/CD pipelines to automate testing, infrastructure changes, and application deployments.
• Designed and implemented the frontend delivery architecture using AWS CloudFront.

Stack: Spring Boot · React · PostgreSQL · REST API · Docker Compose · Terraform · AWS

Participation in the EU-funded MORANE-2 project to validate the Future Railway Mobile Communication System (FRMCS) in real-world use cases: preparation of the requirements specifications for TOBA and the associated test specifications.

Technologies: 5GS (Radio & Core), FRMCS, MCx (MCPTT, MCData, MCVideo), MC Gateway UE, MCx Priority/Security/QoS Management, ETCS, SIP, IMS

Gronic Ident & Sign

Core technologies: Android, Jetpack Compose, Retrofit, Regula Document Reader, Adobe Sign

Android app on dedicated devices for identifications and electronic signatures at the PoS

• Developing a model-based AI where natural language is interpreted as formal relations.
• Natural language terms are not considered rigid but fluid and can be negotiated in a context so meaning resolves by iteratively specifying.
• Develops all kinds of model solutions.
• Backed by natural language and data annotation.
• Requirements to code and other solutions.

• Developed a new iOS access-control scanning app for security/gate staff to validate RFID wristbands/badges in real time
• Implemented an online/offline hybrid architecture: scans are first checked against the API, with a fallback to local SQLite data in case of network issues, including complete access policy evaluation (zone, direction, scan limits)
• Developed a native iOS RFID bridge: connected the proprietary RFID SDK (QuantumSDK) via a custom Expo Config Plugin + Native Module; integrated LineaPro Bluetooth scanner hardware
• Implemented real-time updates via WebSocket (Pusher protocol), including private-channel authentication and reconnect with exponential backoff for device-/gate-/event-based configuration changes
• Designed a 3-level persistence setup (Keychain/MMKV/SQLite) for secure credentials, high-performance state and offline-capable scan/credential data
• Implemented incremental credential sync (Cursor + updated_after) as well as batch upload of scan logs after reconnect to ensure consistent data in field operations

• Contributed to the architecture design process, resulting in scalable and maintainable solutions for complex projects.
• Enhanced app performance by optimizing code and implementing efficient algorithms.
• Stayed current with industry trends to ensure implementation of best practices in app development.
• Led a team of iOS developers for timely completion of projects and achieving client satisfaction.
• Translated basic design plans into full iOS app frameworks, delegating project components to appropriate team members.
• Estimated work hours and tracked progress using Scrum methodology.

• Developed a Flutter app for eID ID card logins
• Focus: Flutter, iOS, eID, AusweisAppSDK, Mason, Keycloak, Agentic Coding, Arc42, Jenkins

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.

Internal systems with regulatory and security requirements could not be used directly by AI tools for incident triage, troubleshooting, and documentation search. The goal was to securely integrate these systems through MCP while preserving existing acce...

• Working on Telecommunication Apps on Android under NDA for the Military
• Participation in the development of backend services (Spring, Java)
• Android Development, Kotlin, Compose, REST, JSON, State-Machines, Android Studio, IP Communication, Cybersecurity, Agile

• Coach international tech professionals through career transitions, helping high achievers move from reactive decision-making to clarity-driven career and life choices.
• Combined software engineering and consulting background with coaching expertise to address challenges specific to the tech industry, including career stagnation, imposter syndrome, and uncertainty around AI's impact on the field.
• Some of my client's measurable outcomes include landing multiple concurrent interview processes after a multi-year career gap, launching independent product ventures, securing technical roles that improved both financial stability and job performance, transitioning into non-tech careers.
• ~200 hours of coaching sessions. Guided clients through 1:1 and group coaching engagements, ranging from single sessions to year-long partnerships, focused on sustainable, values-aligned career growth.

Developed a custom Kotlin Multiplatform app with a Spring backend and AI features.

• RLHF & Model Evaluation: Evaluated, ranked, and refined Large Language Model (LLM) responses based on accuracy, reasoning quality, safety guidelines, and factual correctness.
• Data Annotation & Prompt Engineering: Created high-complexity prompts, edge-case scenarios, and gold-standard reference answers to train and fine-tune generative AI models.
• Quality Assurance & Verification: Conducted rigorous fact-checking, logical consistency validation, and multi-turn response optimization.