GraphQL Experts in Munich

• 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.

• Build and operate an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for the automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, Autoformer).
• Implement CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform), data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) through training and evaluation to model registry and endpoint deployment.
• Integrate MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
• Develop and containerize PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
• Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), log centralization, and cost monitoring.
• Automate infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connect to existing market data systems and event pipelines.
• Migrate existing workloads and databases (IONOS → Azure, MongoDB) with integration into central MLOps workflows and internal networks.
• Extend the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
• Analyze and architect a software solution to process large volumes of data efficiently (>3000 messages/sec.) (market data store).
• Spring Boot / Java 21 container development with RabbitMQ for distributing stock market data via MongoDB (Kubernetes) with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
• Integration of RESTHeart to create a REST API for MongoDB.
• Build an Angular frontend to simplify data queries and master data maintenance.
• Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
• Develop Python scripts for transforming and cleaning incoming stock market data (Pandas, scikit-learn).

Project: Tax Exemption Order Application

The client has an existing application for creating and maintaining tax exemption orders for end customers; design and implementation of a comparable application for internal employees.

• Design and implementation of microservices and the UI for the business area "tax exemption orders" using Domain Driven Design as well as Spring Boot and Angular.
• Implementation of reactive, non-reactive, and asynchronous APIs (Spring REST, WebFlux, GraphQL).
• Development of the Angular application, including state management using Signals, RxJS Observables, and subscriptions.
• Securing the API and the application using OAuth2, JWT, and OpenID Connect.
• Configuration and setup of CI/CD pipelines with Jenkins.
• Collaboration with cross-functional teams and conducting code reviews.

Environment: Java, Spring Boot, Angular 18 & 19 (standalone, signals), RxJs, Bootstrap CSS, Vitesting, OpenShift, Istio, microservices, Kafka, Dynatrace, Jenkins, GitLab, Graylog, Sonar, Oauth2, OracleDB

• Governed multi-agent orchestration for regulated, EU-based companies – self-hostable, compliant with the EU AI Act and GDPR („by design“), BYOM (own models/GPU).
• Two-gate governance: agent deliberation + mandatory human approval, full signed audit trail; graduated autonomy model („internal → autonomous per skill“).
• Verified knowledge graph („Company Brain“) with source evidence for every answer; own orchestration framework (Virtual Team Framework).
• Industry solutions for financial services: compliance monitoring, invoice and contract review; hands-on development with LLMs (including Anthropic/Claude), agentic workflows, RAG.
• Building the governance-focused multi-agent platform TeamIntel (see AI reference projects).

• Delivered backend systems, data processing solutions, and software integrations for enterprise business applications.
• Designed and implemented API-based services connecting internal platforms with external systems.
• Built automated processing workflows to handle large-scale structured business data.
• Improved application performance by 30–50% through database optimization, caching strategies, and backend refactoring.
• Reduced manual operational effort by 40–60% by automating repetitive workflows.
• Supported production environments through troubleshooting, monitoring improvements, and continuous optimization.
• Authored technical documentation and led knowledge-transfer sessions to support long-term maintainability.

• Spring Boot Software Engineer
• DevOps Engineer (Kubernetes, Azure DevOps)

Toolstack: Java, Spring Boot, Kubernetes, Helm, GitOps, ArgoCD, Docker, Azure DevOps, CI/CD Pipelines, Git, Postgres

• Development of dynamic web components for displaying KPIs, intelligent map applications, and operational process analysis tools
• Angular 18+
• Leaflet, MapLibre
• NestJS, JavaScript, HTML, CSS
• PostgreSQL, GraphQL, RabbitMQ
• Gitea, Jenkins, Docker

• Test concept creation
• Creation of test processes
• Coordination of TC development: stress tests, functional tests, performance tests, high data rate tests, integration tests, etc.
• HW testing: FPGA, RF
• Test automation and regression tests
• Ensuring 24/7 operation of the test system
• Analysis & reporting
• Regular coordination of the test team, meetings with other stakeholders
• Communication and coordination with stakeholders and the project manager

• Designed and developed a high-performance internal resource management platform using React and TypeScript, optimizing dynamic data rendering and state management.
• Built a React Native application to support mobile access to internal tools, enabling on-the-go project tracking for field teams.
• Developed custom 2D canvas-based visualizations using Pixi.js to simulate material flows and refractory layer behaviors.
• Integrated Pixi.js with React components for interactive diagrams and real-time UI updates.
• Developed interactive 3D visualizations using React.js for displaying refractory product layouts and simulations, supporting engineering and sales teams with dynamic product previews.
• Integrated Three.js within the React ecosystem to allow manipulation of 3D models in real-time via browser, enhancing user engagement and field configurability.
• Integrated a headless CMS to enable dynamic content updates by non-technical users, reducing content deployment time by 40%.
• Led AWS CloudFront optimization initiatives, improving portal load speeds by 30% globally.
• Actively collaborated with cross-functional Agile teams and product owners to deliver prioritized features with a fast feedback loop.
• Key Technologies: React.js, React Native, Three.js, TypeScript, Contentful CMS, AWS S3/Lambda/CloudFront, Cypress, Agile Scrum

• RTE/Agile Coach for the SAFe 6 (Scaled Agile Framework) rollout
• Building and establishing a LACE (Lean-Agile Center of Excellence) for several ARTs
• Using the tools: Azure DevOps with SCALE, Loop, MS Whiteboard
• Building the ART with 7 teams
• Training Product Owners, e.g. through SAFe POPM training and LearnSnacks
• Running the initial PI Planning as a Kickoff Planning Event
• Introducing a demand process

• Backend development of B2B e-commerce solution using SAP Commerce Cloud, leveraging Java and Spring Framework with creation of REST APIs for effective communication.
• Resolved software defects, developed automated cron jobs and triggers, and authored unit tests with JUnit to uphold code stability and quality standards.
• Utilized Git-based version control systems (Bitbucket/GitHub) for source code management and used Jenkins to support continuous integration and development (CI/CD).
• Executed and closed multiple user stories and tickets across iterative bi-weekly sprints under Agile (SCRUM) methodology.
• Led incident resolution in the production environment and minimized downtime through real-time monitoring (Solr, SAP CCv2, OpenSearch Dashboards) and sustaining SLA targets above 99%.

• Development of a test system
• Establishment of the architecture using Scala/Java for the backend and Swing for the frontend
• Setup of CI/CD pipelines with Jenkins for continuous integration, including automated builds
• Participation in the Scrum team, including daily stand-ups, sprint planning, and retrospectives
• Technology environment: Scala, Swing, EJB, JPA, Scrum, PostgreSQL, Mercurial

• Designed and developed the "Credit Risk Engine" using Camunda, Node.js and Typescript
• Greenfield project for credit card credit assessment for existing and new customers, including EBA KPIs, SCHUFA and CRIF scorings
• Built and modeled workflows (BPMN) and decision logic (DMN) with Camunda Modeler in close collaboration with stakeholders
• Implemented service tasks, user tasks and jobs with Nest.js, Node.js and Typescript, including exception handling
• Backend-for-Frontend (BFF), frontend with React, Tailwind and Ant Design UI library
• CI/CD with GitLab, Kubernetes/Rancher