Event-Driven Architecture Experts in Munich

• 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

• AI Integration (LLM & RAG): Design and build of an internal intelligent RAG system (Retrieval-Augmented Generation) based on LLMs, n8n, and vector data for the automated analysis of technical documents and error logs.
• Design & Implementation: Design of a robust RS-232/UART communication interface for an SBC-based embedded device to control medical shaker systems.
• Architecture & Protocol Design: Implementation of a highly maintainable software structure (OOP, SOLID) and definition of hardware-close, resilient communication protocols including multithreading and advanced error handling.
• Quality Assurance & DevOps: Test automation using xUnit, integration tests directly on the hardware target, and maintenance of technical documentation according to strict medical technology standards via Azure DevOps.

Label: C#, .NET, LLMs, RAG, n8n, RS-232, UART, Multithreading, async/await, xUnit, gRPC/protobuf, Blazor, MudBlazor, EF Core, Visual Studio 2026, Azure DevOps

In my role as Senior Cloud DevOps Architect for BP, an oil and gas company, I had the mission to migrate the Electric Vehicle Charging platform of the EV Division from on-premises and Azure to AWS cloud, resulting in a hybrid multi-cloud, multi-tenant SaaS solution.

Deployment with Kubernetes for the application layer meant provisioning Kubernetes clusters managed by EKS and AKS, with a focus on integrating them into a multi-tenant environment. This integration was achieved by using Kubernetes namespaces and access controls to ensure data isolation and privacy enforcement.

In the database layer, we chose an RDS instance with PostgreSQL to support the backend infrastructure of our applications. Tenants shared the same RDS instance, but each had a dedicated schema.

To ingest near real-time data from physical charge points (CPOs), as IoT devices, via the OCPI protocol, we ran into significant delays with batch processing. As a result, we built a real-time streaming data pipeline using Apache Kafka, while prioritizing an event-driven architecture.

Led collaboration across multiple internal teams, external vendors, cloud providers, and on-site partners to integrate over five systems into a unified solution.

Achievements:

• Successfully designed and implemented hybrid multi-cloud solutions, integrating multiple cloud platforms (AWS, Azure) with on-premises infrastructure, using Site-to-Site VPNs, Firewalls, and Load Balancing.
• Led the migration of on-premises infrastructure to multi-cloud, multi-tenant infrastructure, resulting in 30% faster processing times.
• Migrated workloads from VMware and Hyper-V environments to cloud-based VMs, leveraging cloud-native services to optimize performance, cost efficiency, and scalability.
• Designed a multi-tenant Kubernetes platform leveraging the Kubernetes ecosystem, using Karpenter for dynamic EC2 node provisioning, KEDA for event-driven pod autoscaling (e.g., Kafka message lag), and Rancher for centralized monitoring of multiple clusters (EKS, AKS, or on-prem K8s), replacing Microsoft-centric Azure Arc management service.
• Designed and implemented Python-based FastAPI microservices as part of the EV core-backend on AWS EKS application layer, powering data ingestion and customer analytics pipelines.
• Developed asynchronous, event-driven APIs (Python-FastAPI) for real-time integration with CPOs, supporting OCPI 2.3 and OICP protocols.
• Designed and implemented a secure, production-grade Azure Databricks platform using Terraform, ensuring scalability and cost efficiency.
• Migrated on-premises ERP to a hybrid Dynamics 365 architecture with ERP hosted locally and CRM running in Azure, integrated via Azure Arc.
• Automated CI/CD pipelines for Databricks notebooks and jobs using GitHub Actions & Databricks CLI, reducing deployment time. Reduced infrastructure provisioning time by 70% by automating cloud resource deployment with GitOps.
• Ensured compliance with internal audit and data governance standards (GDPR) through OAuth2/OIDC-based authentication and fine-grained role-based access controls.
• Developed a Zero Trust security model, enforcing least-privilege access and microsegmentation, enhancing security posture and compliance with GDPR and NIST.
• Built interactive analytics dashboards in Amazon QuickSight, integrating data from S3 and Redshift to deliver real-time business insights and visualizations with embedded access for multi-tenant users.
• Led cloud security assessments and full-lifecycle cybersecurity integration during M&A, covering AWS, Azure, IAM (Entra ID), and data protection, while aligning security posture with NIST, ISO 27001, and GDPR across hybrid and cloud-native environments.
• Reduced cloud costs by 64% for a client's dev environment by implementing automated start/stop schedules for EC2 and RDS instances via AWS CDK with EventBridge Scheduler or AWS Systems Manager.

Tech stack:

• Infrastructure as Code: Terraform, AWS CDK, Ansible.
• Containers: Kubernetes on EKS, AKS, Docker.
• Streaming Data Processing: Kafka to Confluent Cloud, after AWS MSK.
• Frontend: TypeScript, React, NextJS, Hooks, Styled Components.
• Backend: Python with FastAPI, also Node.js with NestJS.
• Database: Aurora on PostgreSQL with TypeORM, RDS on SQL Server, Azure Databricks full setup and administration, ETL Pipelines.
• CI/CD and GitOps: GitHub Actions, Azure DevOps, ArgoCD.
• Monitoring and Observability: Prometheus and Grafana.
• Virtualization: Hyper-V, VMware Cloud on AWS, Azure Migrate.
• ERP Systems: Odoo, Microsoft Dynamics 365 Business Central on Azure, integrated with Azure Arc.
• Networking: Site-to-Site VPNs, AWS Direct Connect, Azure ExpressRoute, Firewalls (AWS Network Firewall, Azure Firewall).
• Security: IAM, NIST Framework, Zero Trust Security, AWS WAF, AWS Shield, GuardDuty.

• Management of digital business
• Design, build and run solutions based on ubiquitous technologies

• Clarification of project goals, scope, and functional target vision for building the AI-based knowledge base.
• Deriving the initial architecture and implementation strategy for the Zasterbot chatbot, including defining the MVP and expansion phases.
• Developing a functional target vision for building a structured knowledge base and integrating a future chatbot.
• Deriving and prioritizing use cases for information retrieval and provision by the chatbot.
• Modeling data structures and flows for effectively organizing the knowledge base on the Base44 platform.
• Designing and implementing data models for storing and linking relevant information.
• Developing processes for extracting, analyzing, and preparing raw data for the knowledge base.
• Ensuring data consistency and quality as the foundation for the future chatbot.
• Planning the integration of large language models (LLMs) and retrieval-augmented generation (RAG) for precise and context-aware responses.
• Implementing features for analyzing and visualizing data from the knowledge base.
• Using the Base44 platform with JSON-schema-based entities and a flexible permission model.
• Implementing Deno functions for backend logic, event processing, and external API integration.
• Integrating OpenAI services for initial data analysis.

Technical Program Manager for data- and architecture-driven transformation initiatives in complex system landscapes. Focus on target architecture, data and integration strategy, legacy decommissioning, migration planning, delivery management, and technical governance. Experience in decoupling highly integrated platforms, managing data and process migrations, defining service-oriented target states, coordinating cross-domain engineering streams, and evaluating technical solution options regarding effort, risk, operating model, and feasibility. Proven work in programs focusing on data flows, data provisioning, dealer/customer provisioning, API transition, reporting target architectures, and modernization of business-critical backend and integration systems.

• Definition of goals, milestones, and dependencies
• Program and roadmap management
• Managing cross-country teams to achieve targets
• Aligning the target architecture
• Risk analysis, assessment, and management
• Stakeholder management
• Planning releases, capacities, and dependencies
• Status tracking and C-level reporting
• Defining strategies, tools, frameworks, and integrations
• Quality assurance and acceptance

• Architected and implemented an enterprise-grade AI Agent Platform leveraging Retrieval Augmented Generation (RAG) architecture to enhance clinical data insights.
• Established robust CI/CD pipelines for LLM applications using CDK and Jenkins, significantly reducing deployment times.
• Implemented comprehensive observability solutions that increased agent reliability across pharmaceutical environments.
• Designed scalable AI workflows with advanced orchestration that optimized context handling for enterprise data sources.
• Technologies: AI Agents (LangChain, LangGraph, Bedrock, Smolagents, Streamlit); LLM Operations (Tracing, Testing, Evaluation, LangSmith, LangFuse); Infrastructure-As-Code (AWS CDK, Terraform, Typescript, Jenkins); Vectors, Embeddings, RAG (OpenSearch, pgvector, PDF Extraction)

• Building a scalable and robust financial service based on Bitcoin and paving the way for a truly digital financial world.