Multi-Tenancy Experts in Germany

• Developed a browser-based platform for Kubernetes no-code deployment and cluster management
• Developed a CLI in TypeScript to deploy resources in the cluster without leaving the browser UI.
• Implemented DevSecOps pipelines: image scanning, SBOM, policy enforcement, supply-chain security, and used Kyverno. Implemented IAM integration for the command-line utility tool.
• Designed role and permission models for Keycloak, OAuth/OIDC, and social login flows.
• Used LLMs to convert user intent into diagrams.
• Worked on integration with multiple sovereign clouds like StackIT, Hetzner, CIVO, UpCloud, plus public clouds like AWS, GCP, and Azure
• The technology stack includes Java, Spring Boot, Kubernetes, OpenAI, Kubernetes multi-tenancy using vCluster, Karpenter, RBAC for CLI, Helm, React

• Modernization of a large-scale Java enterprise application*

The project is modernizing a complex enterprise application that has grown over many years. The existing Spring-based legacy system runs on Java 8, OSGi, and Eclipse RCP and is being gradually migrated to a modern, maintainable architecture with Java 25 and Quarkus.

Marcus works on analysis, architecture, refactoring, and implementation. One focus is on untangling historically grown structures and dependencies and on building a clean, sustainable Java and Quarkus technology stack.

Tools & technologies: Java 8, Java 25, Quarkus, Hibernate ORM with Panache, EclipseLink, OSGi, Eclipse RCP, Maven, JUnit, Mockito, REST, JSON, Git, Eclipse IDE, IntelliJ IDEA Ultimate, Jira, Confluence

With project PRIZMA, DB will modernize its infrastructure on the one hand, and develop a fail-safe IT landscape on the other hand, which can be restored quickly and securely in case of a disaster.

• Capture current architectures of existing systems as well as methodical consulting and development of target architectures
• Deepen and maintain the building plan / target IT landscape
• Implement technical architecture concepts & architecture descriptions
• Implement migration concepts for updating and further developing the platform and information systems
• Assess submitted improvement suggestions as part of the project
• Capability management: identify capability gaps, develop target visions, and support transformation planning within the enterprise architecture.
• Create a compatibility matrix of the components in use and compare dependencies of specific versions
• Create an IT concept for extending the platform with the following topics: hardware and software requirements, security, licensing, high availability, load balancing, backup & recovery, update strategy, monitoring integration, etc.
• Coordinate with business architects as well as technical architects from the cross-functional architecture area of the PRISMA program for the topics (backup, Active Directory, monitoring, Citrix, and business applications ...)
• Status meetings and alignment of project planning with the Release Train Engineer / Project Manager
• Advise the Release Train Engineer / Project Manager in identifying project risks
• Advise the System Architect Engineers in steering the implementation of the concept
• Implement the IT concept
• Document the infrastructure

Label: MS Project, LINUX, Windows, ORACLE, Java, REST, SharePoint, Microsoft Exchange, UML, Enterprise Architect, BPMN, AZURE, AWS, V-MODEL, Micro Service, VisualStudio, SAP S/4HANA, SCRUM(SAFE), ESB (TIBCO), Python, Innovator, LeanIX (TOGAF), Ansible, Ansible Tower, Ansible Automation, ROBOT, SpringBoot

• 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.

Seitenkumpel — agents build websites for trade businesses, unattended. Own product, live.

• Agents research public company data and build complete websites from it, with nobody watching
• A validation layer makes sure extraction errors fail loudly instead of passing quietly
• Acquisition runs through a postcard funnel with a screenshot and a QR code, subscription model from 79 euros a month
• Result: several hundred websites built, running unattended
• Honest limit: there are no paying subscriptions yet — the funnel is built, the revenue is not there

Stack: agent workflows built directly without a framework, Claude and OpenAI APIs, TypeScript, Node.js, PostgreSQL, Cloudflare Workers, web scraping, data enrichment

• Designed and developed a multi-tenant SaaS platform enabling organizations to build their own knowledge bases and chat with brand-customized AI assistants (white-label approach with dynamic branding per organization).
• Implemented a scalable RAG architecture with a GPT-4o tool-use loop, hybrid semantic search, and strict tenant isolation at database and search index level.
• Built persistent, project-like chat sessions including a streaming API (SSE), multilingual support, and speech input/output (STT/TTS).
• Delivered the cloud infrastructure as Infrastructure-as-Code, fully automated per-customer CI/CD pipelines, and an onboarding process for new tenants.

Technologies used: Python, FastAPI, Pydantic (v2 noted), Next.js, React, TypeScript, Tailwind CSS, OpenAI / LLMs (GPT-4o), Azure AI Search, Cosmos DB, Azure Blob Storage, Azure Cognitive Services Speech, Azure App Service, Azure Container Registry, Retrieval-Augmented Generation (RAG), Server-Sent Events (SSE), Docker, Terraform, GitHub Actions, REST, OpenID Connect (OIDC), Multi-Tenancy

Development and evolutionary further development of a highly available, cloud-native microservice and integration architecture for dealer and retail processes in the Porsche Car Configurator.

Responsibilities

• Development of Java-/Kotlin-based backend, API, and integration components (Spring Boot)
• Integration of internal and external systems via REST/OpenAPI, GraphQL, Apache Kafka, and AWS SQS (synchronous and asynchronous)
• Implementation of stable, high-performance communication and data flows in a cloud-native platform architecture
• Processing of structured data formats (JSON, Protobuf, GraphQL schemas) based on existing API patterns
• Performance optimization of distributed microservices with reduced response times and higher operational stability
• Technical tests (unit, integration, and API tests) as well as error analysis in production-like environments
• AWS Infrastructure as Code with Terraform and AWS CDK
• CI/CD automation (build, test, and deployment pipelines) with GitHub Actions
• AI-supported feature implementation (GitHub Copilot Agent)

Label: Kotlin, Java 25, Spring Boot 4, Protobuf, TypeScript, AWS, Terraform, CDK, Apache Kafka, AWS SQS, REST/OpenAPI, GraphQL, JSON, PostgreSQL, Docker, GitHub Actions, Maven, Gradle, JUnit, Mockito, Testcontainers

• Conceptual design and implementation of modular AI assistants for sales and business processes in the Microsoft ecosystem (Agentic AI, Copilot extensions)
• Frontend architecture and development with React + TypeScript for embedded chat and assistant surfaces (streaming UI, hooks, React Query, OpenAPI clients)
• Enterprise-level agent development: reusable skill/agent library, MCP server, review and compliance gates
• LLM integration into the user experience: Anthropic (Claude), OpenAI, tool use, RAG pipelines, prompt engineering, guardrails
• Architecture and code review consulting as well as mentoring in the AI development team
• Integration with Microsoft Graph, Power Platform, and Azure services
• Technologies: React, TypeScript, Anthropic Claude, OpenAI, MCP, RAG, Microsoft Graph, Power Platform, Azure

Project for a leading German email and cloud service provider: As Senior Frontend Developer and Technical Web Architect, I developed an international, multi-tenant white-label consent management layer for multiple brands.

Main tasks:

• Architecture and implementation with Vue 3, TypeScript, and Vite
• Development of automated tests with Vitest and Playwright
• Creation of brand-specific CMP configurations, CSS themes, i18n structures, and vendor settings
• Implementation of playout and initialization logic as well as backend integration
• Technical decision support, project, and code documentation

Impact: Replacement of external CMP solutions with a reusable and long-term maintainable in-house foundation for several international brands and rollouts.

Technologies: Vue 3, TypeScript, Vite, Vitest, Playwright, IAB TCF, Google Additional Consent, i18n, Git, CI/CD.

• TrendReel, production LLM agent and RAG system (Python, LangChain, OpenAI, Groq/Llama 3, Claude, FastAPI, Kubernetes, PostgreSQL).
• Designed and built a production multi-step LLM agent system: a script generation agent with a per-platform psychology database, 7 viral narrative frameworks, and structured quality scoring, switching between Claude and Groq backends in real time based on output metrics.
• Implemented multi-provider LLM routing (Claude primary, Groq/Llama 3 fallback) with priority-chain failover and quality-based provider switching, achieving 95% inference cost reduction while holding measurable quality thresholds.
• Built an advanced RAG-style retrieval pipeline with per-platform knowledge bases, semantic content matching, and structured output evaluation across 7 decision frameworks, directly analogous to multi-tenant context-based reasoning for enterprise document workflows.
• BrainyAI, adaptive AI learning platform (Python, LangChain, Groq Llama 3.3-70B, OpenAI, Next.js, Supabase, Redis).
• Integrated Groq Llama 3.3-70B with education-level-aware prompting, dynamically adjusting vocabulary depth, citation complexity, and reasoning style across four student proficiency tiers.
• Nexus Prime, multi-tenant SaaS platform for marketing and growth automation (25 modules, 99 backend routers, 153 frontend files).
• Built a 25-module, 99-router multi-tenant SaaS platform covering ad remix, affiliates, WhatsApp inbox, email, and cart recovery, serving four subscription tiers from $199 to $1,999 per month with integrated Stripe, Paystack, and Flutterwave billing.
• AI Video Surveillance Platform, multi-tenant edge and cloud computer vision system currently in active client pitch.
• Designed a multi-tenant AI video surveillance platform combining edge YOLO26 inference on NVIDIA Jetson Orin NX boxes with a central GKE cloud layer (Postgres, Pub/Sub, ClickHouse, R2, Keycloak) for event storage, dashboards, alerting, and multi-tenancy.

Independent engineering practice focused on custom AI systems, production delivery, and fractional technical leadership.

Selected client engagement: Scalutions

• Role: Serve as fractional CTO and hands-on technical lead, responsible for the architecture and agentic infrastructure behind its managed B2B outbound operation.

• Product: Designed and built OutboundLoop, an agentic SDR operating system for research, qualification, personalized outreach, campaign management, human approvals, measurement, and continuous improvement.

• Scope: Own the full system lifecycle—from business processes and agent behavior to context design, model routing, integrations, evaluation, telemetry, reliability, cost control, and production operations.

Development of an iOS app for virtual try-on and outfit suggestions

The goal of the project is to develop a mobile application for personalized, photorealistic outfit suggestions. Users should be able to upload their own photos, try on clothes virtually, and find products that can be bought directly in the generated suggestions.

• Planning and implementation of the onboarding and photo upload in the iOS app
• Development of the mobile application with Expo and React Native
• Implementation of a Hono/Node.js backend for user, product, and generation processes
• Building an asynchronous processing pipeline with BullMQ and Redis
• Connection of PostgreSQL/pgvector and S3 for product, image, and generation data
• Integration of Gemini and OpenAI for outfit generation and image processing
• Implementation of a credit system and integration of RevenueCat
• Integration of Stripe Connect and affiliate product feeds for products that can be bought directly

Label: TypeScript, React Native, Expo, Hono, Node.js, PostgreSQL/pgvector, BullMQ, Redis, S3, Gemini, OpenAI, RevenueCat, Stripe Connect, Docker

• 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

• Mentor team of 6 ML platform engineers through weekly 1:1s, technical design reviews, and best practices, improving team velocity by 35% through structured sprint planning and skill development programs
• Define 2025–2026 ML platform roadmap in collaboration with Data Science, Cloud Engineering, and Product teams, prioritizing automated model governance, cost attribution systems, and multi-environment deployment strategies
• Partner with Data Science, SRE, and Product stakeholders to align ML platform capabilities with business objectives, reducing data scientist deployment friction by 60% through self-service platforms
• Architect and deliver production-grade MLOps platform supporting 50+ models in production with automated promotion pipelines, versioning, and rollback capabilities, achieving 99.5% platform uptime SLA
• Design distributed ML pipeline architecture using Metaflow and Argo Workflows (Vertex Pipelines-compatible), reducing model training time by 30% and deployment cycles from 2 weeks to 3 days through full CI/CD automation
• Build containerized ML services on Kubernetes with auto-scaling policies, resource quotas, and multi-tenancy isolation, optimizing infrastructure costs by $180K annually (25% reduction)
• Implement monitoring, alerting, and performance tracking using Prometheus, Grafana, and custom instrumentation, reducing model debugging time by 50% and establishing model performance SLOs
• Lead development of RAG-based document intelligence platform using LangChain, LangGraph, and vector databases, implementing agentic AI workflows for automated financial document processing
• Implement Infrastructure-as-Code using Terraform for reproducible environment provisioning and GitOps workflows, reducing infrastructure drift incidents by 80%
• Design role-based access control for ML platform, implement model lineage tracking, and establish audit trails for regulatory compliance aligned with enterprise IAM best practices