Kotlin Experts in Germany

Developed a CPU monitoring application using Spring Boot and React, containerized with Docker Compose, with automated infrastructure provisioning using Terraform on AWS.

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

Digitizing real-world places with 3D/LiDAR scans to make spatial data usable for AI applications and to derive concrete use cases and prototypes from it.

• Digital capture of real-world places as a basis for faster planning and analysis
• Browser-based access to 3D data for easier use and coordination
• Turning spatial data into concrete use cases, prototypes, and AI training scenarios
• Planning basis for urban development and other digital future applications

Keywords: LiDAR, 3D scan, AI, use cases, AI training, prototyping, Python, web development, data models, architecture

• Analysis of affected business processes taking numerous stakeholders into account
• Interface analysis, architecture and system design
• Communicating and coordinating various subprojects and interface partners
• Creating epics and user stories, maintaining the backlog, workshops and review presentations
• Support during implementation between business departments and development
• Test management including strategy and approach definition
• Test case definition, execution and approval
• Cross-team organization of integration and acceptance tests
• Support of test environments
• Technologies and tools: Java, Angular, Kubectl, REST, AWS SNS/SQS, Kafka, S4/HANA, Bruno

• Building a data integration platform for high, medium, and low voltage assets for contextual analysis of time series with master data from the SCADA control system (IEC 60870 104), INIS, and SAP.
• Responsibility for the architecture and implementation of the solution, as well as sparring partner for the Product Owner.
• Use of Kotlin, Spring Boot, Maven, TimescaleDB, PostgreSQL, liquibase, Elements IoT, Docker, Kubernetes, Grafana, Python, jupyter, and various API gateways.

• Ride-sharing and bus platform, live and working. Backend Spring Boot 4.1 on Java 21, PostgreSQL with Flyway, Testcontainers integration tests. Running 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

• Training the AI models, evaluating images and texts for UI/UX, turning the provided data into insights via OpenAI Feather as part of the machine learning workflow

Technologies: OpenAI Feather

• Focus: system architecture, cloud-native platforms, microservices, API design
• Product: Pflege-Pfad is a digital matchmaking platform that connects relatives of people in need of care directly with verified care services and caregivers - without an agency and without ongoing fees.
• Business analysis & process design:
• Analysis of the German care market and identification of the key pain points of both target groups.
• Modeling of the core business processes: registration, verification, care request, application, placement, and rating.
• Definition of the business model as a freemium/premium model with optional contact unlocking.
• Creation of user stories and requirements documentation for relatives, care services, and administrators.
• Design of trust and quality assurance mechanisms with document upload, admin review process, and rating system.
• Coordination with stakeholders and validation of product decisions with potential users.
• Technical implementation:
• Design and implementation of the entire platform architecture as a solo developer.
• Design and implementation of a REST API with Spring Boot and Kotlin, including JWT-based authentication.
• Development of the frontend as a single-page application with Angular 17.
• Implementation of the AWS infrastructure with EC2, RDS PostgreSQL, S3, CloudFront, and IAM.
• Document upload with AWS S3 via presigned URLs for verification of care services.
• Email notifications via Resend API.
• AI-supported care service search via OpenAI API.
• Implementation of complete user flows such as registration, login, password reset, and placement process.
• Building an admin panel for user and care service management as well as analytics.
• CI/CD with GitHub Actions and containerized deployments with Docker.
• End-to-end tests with Playwright.

Technologies: Kotlin, Spring Boot 3, Spring Security, JWT, JPA/Hibernate, PostgreSQL, Angular 17, TypeScript, RxJS, AWS (EC2, ECS, S3, CloudFront CDN, RDS PostgreSQL, IAM), nginx, GitHub Actions, Playwright, Maven, Git, OpenAI API, Resend API, Docker, Scrum, i18n (DE/EN/TR), Kiro, feature-flag architecture.

maturing their API Practices on both, a Business and Technology level. My role encompasses strategy, architecture, developer advocacy as well as hands on software engineering, enabling both technical teams and business leadership to adopt and act on API- centric principles effectively. Coincidentally, we also establish GitOps, DX and Platform Best practices with this project.

Highlights:

• Aligning executives with the initiative by clarifying strategy, replacing misconceptions and myths with facts, clarifying the value of existing assets and enabling informed decision-making
• Formulating a way forward for API Lifecycle Management at OTTO
• Driving platform progress and fostering developer engagement by hands-on engineering work towards strategic goals

API Lifecycle Management, Team Topologies, Organizational Evolution, Regulatory, Platform Advocate, Developer Platform, Communities of Practice, Terraform, Kotlin, Kafka, Kong, WSO2, Apigee, Gravitee, Backstage, AsyncAPI, OpenAPI, API Design, AWS, react, nodejs, typescript, redocly, reactive programming, CDC, golang, gingonic, GitOps, DX (developer experience), stakeholder management, roadmaps, workshops, discovery.

• Designing the audit logging framework
• Implementing APIs for developers to integrate in their codebase
• Implementing ingestion pipeline, database query layer and UI for browsing the audit events
• Improving stability and reliability of the backend system

• Designed, built, and launched an internal event planning web application used by over 100 department leads for a large event, despite having no dedicated testing phase.

• Ensured smooth, failure-free operation during first production use, leading to the tool being adopted for future events.

• Automated catering calculations and related workflows, significantly reducing email communication and manual computation effort for meal planning.

• Managed deployment and hosting on a Linux server using Coolify, including application setup and runtime operations.

• Hired and guided a communication designer on UX while independently owning all technical decisions and implementation.

• Developed backend features for Modern RIS, a web-based Radiology Information System integrated with the existing Classic RIS via WebView.
• Worked on a modular Spring Boot backend covering clinical workflows such as appointments, examinations, patients, orders, reporting, billing, and inventory.
• Contributed to event-driven architecture using domain events to decouple workflows across backend modules.
• Implemented REST/OpenAPI endpoints, service-layer business logic, DTO mapping, validation, and integration points for the React frontend.
• Worked with PostgreSQL-backed domain models, Liquibase database changes, read/write model separation, and legacy RIS database structures.
• Integrated authentication and authorization flows using Keycloak and OAuth2.
• Added and maintained unit/integration tests using JUnit, Rest Assured, Testcontainers, and project-specific test utilities.
• Supported CI/CD and local development workflows using Maven, Docker Compose, Jenkins, and generated OpenAPI clients.

Tech stack: Java 21, Spring Boot 3.5, Maven, PostgreSQL, Liquibase, Keycloak, OAuth2, REST, OpenAPI/Springdoc, MapStruct, Lombok, Docker, Testcontainers, Jenkins.

• Led discovery phase and POC for Shopware to Shopify Plus migration across EMEA markets, evaluating platform suitability, technical architecture, and multi-brand/multi-country capabilities against business requirements.
• Designed reference architecture for Shopify Plus implementation incorporating headless front-end patterns (Vue.js, Nuxt.js), CMS integration (Magnolia), and Azure middleware (APIM, Functions, Logic Apps, Service Bus) for 11 EMEA markets.
• Defined migration strategy analyzing data mapping, cutover approach, and zero-downtime deployment patterns using Varnish caching, GitOps pipelines, and CI/CD orchestration across six vendor teams.
• Architected multi-tenant Shopify Plus governance model with centralized admin, localized storefront customization, and compliance controls (GDPR, data residency).
• Prototyped AI-driven search optimization (LLM.txt, JSON-LD) for product discoverability in Google AI results, demonstrating post-launch performance opportunities.
• Defined EMEA expansion roadmap for 15+ markets through C-level strategic workshops, identifying phased rollout, market-specific configurations, and resource requirements.
• Tech Stack: React, Nuxt.js, Vue.js, Magnolia CMS, Shopware, Shopify Plus, Azure (APIM, Functions, Logic Apps, Service Bus, Front Door), Varnish, SAP, MS Dynamics, Docker, Kubernetes, GitHub Actions, PostgreSQL, Kafka

• Delivered MVP-focused full-stack architecture for a health-sector client: Vite/React frontend, backend services on Google Cloud Run, and Supabase for database plus IAM/authentication.
• Supported product requirements engineering and prioritized cost-aware workload placement, implementing browser-side/edge computation where feasible before moving logic to backend services.

• Built and fine-tuned CNN and RNN architectures using transfer learning for real-world classification tasks — core deep learning skills applicable to BMW's multimodal LLM and GenAI vehicle function development.
• Implemented Dropout, Batch Normalisation, and Early Stopping across deep learning experiments; evaluated rigorously using precision, recall, F1-score, and confusion matrices for production-grade reliability.
• Developed an AI-powered attendance management system using LBPH facial recognition, deployed via Flask web interface with real-time SMS notifications — demonstrating end-to-end AI product delivery for real users.
• Collaborated across cross-functional teams to deliver scalable, documented ML pipelines designed for reproducibility — matching BMW's interdisciplinary team and research environment.
• Integrated AI tooling directly into the development workflow from design through to testing, maintaining high velocity without compromising correctness.