iSAQB Certified Professional for Software Architecture – Foundation Level (CPSA-F)

• Development of an agent-based governance platform for the automated assurance of EU AI Act compliance and ODA-compliant orchestration of AI services in complex enterprise environments.

• Design and implementation of an agent-based "Mission Control" framework (Aletheia Conductor) for autonomous state monitoring and process control.

• Development of "Compliance-as-Code" (CaC) solutions based on OPA/Rego for system-wide enforcement of regulatory guardrails.

• Integration of TM Forum ODA standards (TMF630, TMF622, TMF642) to ensure interoperability and standardization.

• Building a highly available event-driven architecture using Redpanda and CloudEvents v1.0 for near-real-time event processing.

• Implementation of an audit-proof "Evidence Chain" through cryptographic linking of trace logs in preparation for automated audits.

• Tech Stack: Java 21 (Quarkus Native), TypeScript (Next.js), Redpanda (Kafka API), CloudEvents v1.0, OPA (Open Policy Agent) & Rego, TimescaleDB, ZincSearch, Redis, TM Forum ODA, Git, GitHub, Clean Code Development, Like-C4.

Stabilization of an Azure/.NET landscape in live operation.

• Architecture, DevOps, and operational readiness; technical decisions under time pressure
• Azure DevOps, monitoring, ETL/ELT, cloud security, FinOps, and data-mesh-related topics

Technologies: Azure DevOps, .NET, CI/CD, monitoring, FinOps

Built an international e-commerce platform for a multi-vendor marketplace for digital assets from scratch. Designed and operated cloud native architectures at enterprise scale.

• Designed and operated a highly scalable microservice and serverless architecture
• Built the complete cloud infrastructure with Terraform + AWS CDK in AWS
• Provisioned ECS/EKS clusters (Fargate), Application Load Balancers (reverse proxy), and Lambda functions
• Observability & tracing with CloudWatch, DataDog, Prometheus, and Grafana
• End-to-end setup with DataDog (formerly AWS CloudWatch), Prometheus, and custom Grafana dashboards
• Integration of advanced metrics (including ORM mapper) and distributed tracing with Jaeger
• Robust backup and disaster recovery strategies
• RDS Postgres backups and hourly snapshots
• Read-only, asynchronously synchronized replicas with automated master failover in emergencies
• Minute-level rollback capability through versioned Docker images on ECS and Git-based CI/CD pipelines
• Created CI/CD pipelines with GitHub Actions for automated multi-stage deployments (Dev, Testing, Prod)
• Integrated Stripe for international payment processing
• Built a marketplace payment system with multiple parties and payout routines
• Used Algolia for high-performance real-time search of digital assets on the platform
• Federation of services with GraphQL and Hasura
• Later migration to GraphQL Mesh
• Test Driven Development (TDD) - unit, integration, and E2E testing with Jest, Vitest, and Playwright
• Used Next.js / React for modern frontend applications in the nx monorepo
• Enterprise security architecture & access control
• Integration of JWT tokens with Auth0, OAuth, OIDC, IP guards, BOLA protection, and secret vaults
• Authorization concepts with RBAC, ABAC, and native Postgres Row-Level Security (RLS)
• Built internal microfrontends with Retool for fast prototyping and operational business processes

Technologies: ABAC, AWS CDK, AWS CloudWatch, AWS ECS, AWS EKS, AWS Fargate, AWS RDS, AWS S3, Algolia, Auth0, DataDog, Docker, GitHub Actions, Grafana, GraphQL, GraphQL Mesh, Hasura, JWT, Jaeger, Java, JavaScript, Jest, Kotlin, Kubernetes, Monorepo, Next.js, OIDC, Playwright, Postgres, Postgres RLS, Prometheus, RBAC, Redis, Retool, Serverless, Stripe, Terraform, TypeScript, Vitest

• Recurring problem in my own AI-assisted projects: requirements analysis, use cases, and architecture decisions can be created quickly with AI support, but remain difficult to follow and scattered across Markdown files – knowledge is lost as soon as it is no longer in the context window. arknet turns requirements engineering and architecture knowledge into structured, verifiable data instead of plain text: requirements, use cases, and architecture decisions form a consistently linked knowledge graph, traceable from requirement to architecture decision – queryable by both people and AI agents. Technically based on RDF/OWL and a custom MCP server.

• Result: Working MCP daemon, Docker image published automatically to GHCR, nine hexagonal modules, eleven ADRs (including an Open-Core licensing model). Requirements engineering and Ubiquitous Language hexagons are active. Public as a Community Edition under Apache-2.0 since 07/2026 (github.com/kogn-io/arknet), together with the Claude Code plugin and GHCR image; Open-Core model.

• Label: Java, Maven, RDF, RDF4J, OWL, SPARQL, Model Context Protocol, Spring AI, Docker, GitHub, Git, Claude Code, Obsidian, DDD, Hexagonal Architecture, ArchUnit, JUnit, AssertJ, Interface Development, Software Architecture, Continuous Integration, Knowledge Management

• Development of safety-related embedded software for a mobile machine on a Hydac TTC 2390 controller.
• Implementation in C with consideration of SIL 3 / ASIL D and MISRA-C:2025.
• Implementation of communication via CAN, CANopen, and J1939.

Standards, guidelines and norms: ISO 26262, IEC 61508, MISRA-C:2025

Technologies: C99, Hydac TTC 2390, Infineon TriCore Aurix TC397, Hightec C/C++ Compiler, PC-lint Plus, CAN, CANopen, J1939, MISRA-C:2025

• Client: Prime Force Group GmbH

Technologies used: Java 25, Spring Boot 4, MapStruct, JSpecify, PostgreSQL, Redis, Liquibase, REST/OpenAPI, Apache Kafka, Apache Solr, OpenID Connect via IronGate/Keycloak, SAP Customer Data Cloud, JUnit, Testcontainers, Karate, Playwright, GitLab monorepo with CI/CD, Jenkins, JFrog Artifactory, FluxCD, Docker, Kubernetes on Azure, OpenTelemetry, arc42, Jira, Confluence

The Team Management Center is the new central platform of the DFB for planning, managing, and carrying out team activities for the national teams - from squad selection and training camps to communication with players, clubs, and legal guardians. The platform is designed for multi-tenancy for the DFB and regional associations; player, club, and master data are intentionally not copied, but connected at runtime via the DFBnet APIs.

I have been involved in the project continuously since the architecture and concept phase (Sprint 0) and work in a distributed Scrum team in two-week sprints. In addition to implementation, my focus is on architecture alignment, connecting the DFBnet interfaces, as well as code reviews and test automation as quality assurance in the team.

Focus areas:

• Development and implementation of the multi-tenancy concept (tenant model for the DFB and regional associations), including data model, access layer, and Liquibase migrations.
• Design of the person service and the search concept based on Apache Solr.
• Integration of the DFBnet APIs (player, person, and club search, club data), including authentication and synchronous master data synchronization.
• Hardening the integration through resilience patterns: separate read timeouts for each search path, correction of circuit breaker counting, limiting parallel requests, and a club cache to reduce load on the external system.
• Development of self-service endpoints for players (own activities, activity details, games), including an access concept for participants, as well as person documents and file uploads.
• Standardization of API design: OpenAPI annotations, nullability model via a custom ModelConverter, JSpecify migration of the DTOs, and documented API guidelines.
• Build and maintenance of Karate-based API and integration tests, integration tests with Testcontainers, test guidelines, and bug triage from the integration and reference environments.
• Code reviews via merge requests, architecture documentation according to arc42, and architecture decisions (ADRs) in Confluence.
• Automated deployment to the integration and reference environments, analysis of login and OIDC issues in combination with IronGate.

Status: ongoing - as of 08/2026 in Sprint 17, around 940 person hours worked; testable delivery to the integration environment every two weeks.

• Development and optimization of the RTL+ frontend application for SmartTV and set-top box platforms with React and Next.js.
• Key role in the technical coordination of developers within the team and in coordinating implementation.
• Central interface to adjacent teams to simplify development processes and improve cross-team alignment.
• Improved frontend performance, stability, and rendering behavior on low-powered devices in a restricted runtime environment.
• Implemented a frontend testing strategy with Jest, React Testing Library, and Playwright.
• Implemented accessibility improvements according to WCAG 2.2 and WAI-ARIA.
• Integrated Didomi Consent Management as a contribution to increasing ad monetization on streaming platforms.
• Used AI-supported engineering workflows with Cursor for structured implementation, refactoring, and faster problem solving.

Technologies used: React, Next.js, TypeScript, JavaScript, GraphQL, Apollo Gateway, Zustand, Tailwind CSS, Styled Components, React Testing Library, Playwright, Jest, HTML5, CSS3, AWS Lambda, EC2, CloudFront, S3, GitLab CI/CD, NX, Cursor

• Product responsibility for the central platform "one" as a group-wide web-based customer portal
• Coordination of the connection of 20 group companies to the product platform
• Derivation and steering of a group-wide product strategy and roadmap aligned with company goals
• Prioritization and bundling of strategic requirements from the various group companies
• Harmonization of different interests and moderation of complex decision-making processes at management level
• Ensuring the technical and business integration of the product into existing system landscapes, business processes, and business models
• Building transparent governance and decision-making structures for group-wide product development
• Representation of the product towards internal and external stakeholders at leadership level

• Own the software system architecture for laboratory automation products; specify interfaces across software, middleware, hardware and motor control in a regulated IVD environment.
• Led architecture evaluations and proof-of-concepts for integrating AI capabilities (anomaly detection, predictive maintenance) into lab automation under medical-device quality standards.
• Introduced GenAI-assisted development tools across the team, improving productivity and code review quality.
• Communicate architecture decisions to product and project management; coordinate research and improvement projects with system, electronics and external partners.

• 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.

• Led and mentored junior developers and architects.
• Planned, coordinated, and delegated tasks to internal and external team members.
• Selected the technology stack for integrating new software products into the Industrial Hydraulics product ecosystem.
• Accelerated the integration process by initiating successful automation initiatives.
• Ensured software delivery complies with cybersecurity, regulatory, and governance requirements.
• Led the design and implementation of a Service-Oriented Architecture (SOA) for the software ecosystem, fostering collaboration across business units.
• Defined integration strategies for internal and external systems, including Customer Relationship Management (CRM) and Identity and Access Management (IAM) platforms.
• Collaborated with external suppliers and internal IT teams to design and implement robust solutions and application interfaces.
• Advocated for business unit requirements in central projects, ensuring their successful fulfillment.

• Product Owner for the Digital Delivery team
• Company-wide Bosch digitalization project for software deliveries
• Technologies: SAP EMS, JFrog, Revenera
• Conducting PI Plannings

Re-platforming an e-commerce shop to a microservice architecture

• Goal: Replace an outdated Shopware system with a scalable, future-proof solution based on microservices and a headless architecture.
• Led a full architecture consulting process and defined the microservice boundaries based on a headless architecture with commercetools as PIM/OMS and Next.js as the frontend solution.
• Developed and integrated several decentralized services (e.g. internationalization, personalization).
• Took over the configuration of central third-party systems such as Contentstack and Algolia.
• Built a stable cloud infrastructure on Google Cloud with monitoring via Grafana.

Technologies: commercetools, Next.js, Contentstack, Algolia, Google Cloud, Grafana, TypeScript, Shopware

Development of an international e-commerce platform

• Goal: Build a high-performance, user-friendly and international e-commerce platform.
• Defined a scalable, high-performance and maintainable software architecture that served as the foundation for the platform's international expansion.
• Selected a best-of-breed technology stack that enabled the development of an industry-leading shop and reduced development effort for new features by 30%.
• Ensured seamless integration of critical third-party systems (PIM, CRM, ERP) to guarantee end-to-end business processes and a consistent data foundation.
• Implemented comprehensive tracking and analytics tools for continuous performance monitoring and optimization of the customer journey.

Technologies: React.js, Next.js, commerceTools, Algolia, Salesforce, Heroku, CI/CD, PHP, Google Analytics

AI-powered personalization and customer data platform in e-commerce

• Goal: Replace static content with a dynamic, AI-based personalization strategy to increase user relevance and automate marketing processes.
• Designed and built a customer data platform to aggregate and combine customer and analytics data from distributed sources.
• Implemented automated categorization of customer profiles as the basis for delivering personalized content and product recommendations in the Shopware frontend.
• Developed a semantic similarity algorithm based on Python and OpenAI to calculate product and content similarity from user profiles.
• Built the technical connection to retail media platforms to control external ad placements along the customer journey.

Technologies: Shopware 6, Python 3, OpenAI, Elasticsearch, PHP, Symfony, Twig

Shopware tracking & consent architecture (GDPR) for 4 online shops

• Goal: Build a unified, GDPR-compliant tracking infrastructure across multiple shops with central consent management across several Shopware instances.
• Defined a comprehensive tracking guide and developed a modular architecture compatible across multiple Shopware versions.
• GDPR-compliant integration of Usercentrics and Adobe Launch through a central tag manager.
• Full tracking setup (page, order, product, user) incl. partner-specific tracking (Emarsys, Channelpilot, etc.).
• Detailed event and error tracking to proactively identify technical drop-offs.

Technologies: Shopware, Adobe Analytics, Usercentrics, Tag Manager, PHP, MySQL, GDPR

AI/LLM search engine with RAG and hybrid search (Python, Elasticsearch)

• Goal: Build an AI-powered search engine with RAG architecture and hybrid search to accurately match service providers from over 500,000 company records.
• Developed an automated data pipeline (web scraping + LLM) that continuously crawls company data and converts it into structured formats using LLMs.
• Implemented a RAG workflow incl. vectorization for semantic search to increase search accuracy and relevance.
• Configured and fine-tuned Elasticsearch for hybrid search (vector + keyword search).
• End-to-end development of backend API, frontend and deployment on live servers.

Technologies: Python, FastAPI, Elasticsearch, LLM, RAG, React, Docker, Web Scraping

AI/computer vision system (Python, ML) – object detection under difficult conditions

• Goal: Develop an AI-powered recognition system with reliable performance even in rain, fog, snow and darkness.
• Built and annotated a large training dataset incl. difficult conditions.
• Trained a YOLO-based object detection model; carried out systematic error analysis and improved data quality and preprocessing.
• Coordinated with stakeholders through regular status updates.

Technologies: Python, Machine Learning, TensorFlow, PyTorch, YOLO, OpenCV

• Promoting a leadership culture
• Building structures for the agile teams
• Training the mindset for a product-centered way of working
• Establishing a Definition of Done
• Embedding team responsibility
• Using ConceptBoard as a tool for workshops, retrospectives, and training