Our enterprise customer B. Braun developed an Internal Development Platform (IDP) to enable business units and development teams to build applications efficiently, securely, and in a standardized way. As a Cloud Engineer / Platform Architect, I was responsible for technical design, enablement, and continuous improvement of the IDP. Ensured platform usability, compliance, and operational excellence.

• Implementation and development of the B. Braun Internal Development Platform (IDP)
  • Provided self-service environment with templates and best practices
  • Streamlined dev processes and enhanced Developer Experience (DX)
  • Integrated security-by-design principles and automated CI/CD pipelines
  • Further developed and optimized existing IDP components for scalability
  • Designed & implemented modular Terraform infrastructure templates
  • Built GitHub Actions and Terraform modules to automate pipelines
  • Conducted workshops and enablement sessions for cloud teams
  • Trained application teams to efficiently adopt and use the IDP
  • Provided consulting support for migrating applications into IDP platform

Methods and Tools

• Used Kanban methodology with daily standups for agile delivery
• Applied DevSecOps principles, including security-by-design and PaaC
• Designed, reviewed, and automated CI/CD pipelines for reliability
• Managed infrastructure changes using GitOps approach for consistency
• Conducted live demos, hands-on sessions, and brown bag lunches
• Reviewed code and IaC through pull requests and automated checks

Technologies Applied

• Azure Cloud: AKS, Container Apps, Storage Account Gen2, Azure Cosmos
• Azure PostgreSQL and VNet/Subnet for scalable cloud infrastructure
• Infrastructure-as-Code: Terraform for provisioning IDP components
• CI/CD & DevOps: GitHub Enterprise Actions, Repos, Code Scanning
• Static Code Analysis: SonarQube and Snyk for security scanning
• Kubernetes: YAML for Helm charts and deployment management

Our enterprise customer B. Braun, required a scalable Landing Zone platform on Azure to automatically provision standardized environments (DEV, QA, PROD) for multiple business units. The Landing Zone needed to be secure, compliant, and aligned with Azure best practices, providing a stable foundation for multiple subscriptions while enabling flexible development spaces for teams. An interdisciplinary team implemented this strategically important infrastructure, ensuring alignment with the customer?s cloud strategy and operational requirements.

• Design, Development & Operations of the B. Braun Azure Landing Zone Platform
  • Interface between Landing Zone & Internal Development Platform (IDP)
  • Gathered and aligned functional and non-functional requirements across units
  • Analyzed technical dependencies between existing services & new Landing Zone
  • Supported automated provisioning logic development for Azure goverment
  • Advised business units on architecture, security, and cloud prerequisites
  • Coordinated with Microsoft partners to validate solution and technical approaches
  • Conducted quality assurance and reviews of deployment pipelines and resources
  • Managed stakeholder communication and technical alignment across teams

Methods and Tools

• Applied Agile Scrum methodology via Azure DevOps project management
• Conducted stakeholder workshops for requirement clarification and progress
• Documented technical requirements using Miro boards and Azure DevOps
• Created architecture documentation with ADR and UML diagrams
• Participated in Microsoft partner meetings to validate solution approaches
• Reviewed deployment pipelines and automated provisioning scripts

Technologies Applied

• IaaC: Azure Bicep modular templates for Landing Zone provisioning
• Azure Services: Management Groups, Subscriptions, RBAC, Policy, VNets
• Scripting: PowerShell for subscription automation and IAM configuration
• CI/CD & DevOps: Azure DevOps pipelines for source control integration
• Azure Governance & Best Practices: Blueprints, CAF, ALZ, Well-Architec

B. Braun planned the development of a company-wide Data Intelligence Platform based on a Data Mesh approach to enable decentralized data ownership across departments. The platform provided a scalable foundation for AI applications while ensuring data governance, security, and transparency. A Microsoft-sponsored Proof of Concept (PoC) validated the architecture and implementation approach.

• Architecture and Design of the for the B. Braun Data Mesh, Data Intelligence Platform
  • Designed end-to-end cloud architecture for Data Intelligence Platform layers
  • Defined data security, governance, and management policies and standards
  • Designed Azure resource architectures with networking, cost, and security focus
  • Supported Databricks PoC with RAW, Silver, and Gold data layers
  • Implemented Unity Catalog for metadata, classification, and secure access control
  • Collaborated with Microsoft architects and business stakeholders on requirements
  • Introduced Data Mesh principles enabling decentralized ownership and scalability
  • Ensured compliance with Microsoft cloud security and governance best practices

Applied Methods / Tools

• Agile / SCRUM methodology with iterative planning and delivery cycles
• Azure DevOps for repositories, pipelines, backlog tracking, and automation
• Architecture modeling with diagrams for data flows and security models
• Data Mesh principles for self-service data and domain ownership
• Databricks workflows with orchestration, pipelines, and Delta Lake processing
• Documented architecture and governance frameworks across platform layer

In the medical technology sector at B. Braun, this project focused on migrating an existing IoT proof-of-concept solution into a scalable, production-ready cloud platform. The goal was building a secure, resilient, and automated infrastructure for IoT device data ingestion, processing, storage, and visualization. The transformation included Infrastructure as Code (Terraform), CI/CD pipelines with Azure DevOps, and integration of multiple Azure services for analytics, monitoring, and alerting. Regular stakeholder alignment ensured transparency, compliance, and overall success.

• Re-Architecture and Migration of B. Braun Venture MedTech IoT Platform
  • Migrated IoT solution from proof-of-concept to Azure production platform
  • Implemented IaC with Terraform for scalable infrastructure provisioning
  • Built CI/CD pipelines in Azure DevOps for automation
  • Integrated Azure IoT Hub for device connectivity and telemetry ingestion
  • Designed data flows with Data Factory and Databricks processing
  • Implemented monitoring and alerting using Monitor, Insights, and Azure Alerts
  • Deployed front-end applications via Azure Static Web Apps service
  • Coordinated stakeholder meetings ensuring compliance and project transparency

Applied Methods / Tools

• Infrastructure as Code implemented with Terraform automation modules
• CI/CD automation pipelines designed and operated via Azure DevOps
• Agile/Scrum methodology for project delivery and team collaboration
• Git-based workflows for branching, version control, and peer reviews
• Automated testing pipelines to ensure reliable and secure deployments
• Regular reporting for stakeholder alignment and progress communication

Applied Technologies

• Azure IoT Hub for secure device connectivity and ingestion
• Azure Storage Account for scalable and compliant data storage
• Azure Data Factory for orchestration of complex data workflows
• Azure Databricks for large-scale transformation and advanced analytics
• Azure Logic Apps to automate workflows and application integrations
• Azure Static Web Apps for secure and scalable frontend hosting
• Azure Monitor, Insights, Alerts for monitoring, logging, observability
• Terraform and Azure DevOps for provisioning and automation pipelines

At ZEISS, an AI MedTech - MLOps inference platform was developed on Microsoft Azure, built on a central data layer for structured medical device data storage. The platform aimed to enhance medical devices with Artificial Intelligence (AI), enabling intelligent decision support systems for advanced medical applications. As a Senior Cloud Engineer & MLOps Engineer, I designed, developed, and implemented a secure, compliant, and scalable cloud-native platform, ensuring full alignment with regulatory requirements and enterprise strategy.

• Design and Development of an AI MedTech - MLOps Inference Platform
  • Facilitated daily SAFe / Scrum ceremonies for project management alignment
  • Authored medical documentation compliant with ISO 13485 / IEC 62304
  • Defined and implemented Azure cloud strategy for regulated environments
  • Designed secure and compliant cloud-native architectures on Microsoft Azure
  • Moderated architecture workshops to capture business and technical needs
  • Coordinated stakeholder communication with partners and internal experts
  • Conducted architecture reviews ensuring compliance and continuous improvement
  • Defined and delivered MLOps features, epics, and user stories

Applied Methods / Tools

• V-Model combined with Agile Scrum for structured delivery
• Creation of regulatory documentation according to medical device standards
• Architecture and requirements workshops for solution alignment and feasibility
• MLOps lifecycle management for scalable and secure AI workloads
• DevOps and IaC practices ensuring automation and reproducibility

Applied Technologies

• Azure: AKS, Event Hub, Service Bus, Synapse, Storage Gen2
• K8s: Python microservices, Helm charts, and DAPR framework
• ML Models:
  • U-Net segmentation, anomaly detection on medical images
  • Classification for automated diagnostics in medical workflows
• CI/CD: Azure DevOps Boards, Pipelines, Repositories for automation
• IaC: Terraform modules and Kubernetes YAML for infrastructure provisioning

At ZEISS, an on-premises GPU cluster with five high-performance computing units was deployed to support resource-efficient and cost-optimized AI model development. This dedicated infrastructure enabled architecture teams to perform training, deployment, and experimentation with advanced AI models.

• K8s GPU On-Premises Cluster for AI Model Training
  • Operating and maintaining on-premises GPU nodes, hardware and software
  • Configuring Linux OS, SSH, networking, IP, and security hardening
  • Deploying Kubernetes and Docker environments for scalable AI workloads
  • Delivering enablement workshops like ?Train Your Model on Prem?
  • Monitoring GPU utilization, system performance, and troubleshooting bottlenecks
  • Supporting ML pipeline integration into GPU cluster infrastructure
  • Implementing RBAC policies and secure communication for multi-user access
  • Ensuring system stability, scalability, and secure AI operations

Applied Methods / Tools

• Provisioning and configuration management for GPU-based infrastructure systems
• Docker containerization to ensure reproducible machine learning environments
• Kubernetes orchestration for scheduling, scaling, and workload distribution
• CLI-driven workflows using YAML, Dockerfiles, and shell scripting
• Monitoring with htop, kubectl top, and nvidia-smi utilities
• Enablement through hands-on workshops and continuous knowledge sharing

Applied Technologies

• Ubuntu Server 18.04 LTS for GPU node operating system
• Linux-based networking configuration with strict enterprise security setup
• Docker 24.x and Kubernetes (KubeAdm) for orchestration management
• NVIDIA CUDA Toolkit supporting efficient GPU utilization in AI training
• Monitoring tools: tmux, htop, kubectl, nvidia-smi for performance checks
• Version control with Git for collaboration and reproducible builds

At BMW Group, the EVE (Emotional Virtual Experience) application was developed to automatically generate high-quality images and videos of BMW vehicles using Unreal Engine. The solution delivers content to internal BMW products (e.g., Digital Brochure) and external partners via APIs. A scalable AWS infrastructure ensures high performance, availability, and cost efficiency.

• Architecture and Team Leadership for BMW?s EVE Platform
  • Designed scalable AWS architecture for GPU-intensive rendering workloads
  • Defined and implemented BMW?s cloud strategy for hybrid environments
  • Conducted architecture reviews ensuring quality, scalability, and compliance
  • Led workshops to improve Scrum processes and team collaboration
  • Established FinOps practices for GPU and cloud cost optimization
  • Coordinated stakeholders and partners for smooth integration and delivery
  • Monitored production systems, ensuring stability, performance, and alerting
  • Delivered KUNNO orchestration solution for on-premises EVE deployments

Applied Methods / Tools

• SAFe / Scrum methodology for agile project and team management
• AWS CDK for infrastructure provisioning using Python templates
• GitHub Actions / Pipelines for CI/CD automation workflows
• Cloud and solution architecture design, review, and optimization
• DevOps and FinOps practices for scalable, cost-efficient operations
• Incident management and operational governance for production systems
• Team coaching and enablement workshops for knowledge sharing
• Ensured security, compliance, and cost efficiency across cloud solutions

Applied Technologies

• AWS: Lambda, EC2, API Gateway, S3, SQS, Step Functions
• Monitoring: CloudWatch with dashboards, metrics, and alerts
• Unreal Engine: Automated rendering workflows using Packer and CodeBuild
• Infrastructure as Code: AWS CDK (Python templates)
• CI/CD: GitHub Repos, Actions, and Pipelines for deployments
• Collaboration tools: Jira, Confluence, Bitbucket for project management

At BMW Group, the Delivery App was developed to optimize and digitalize vehicle deliveries, improving efficiency and customer experience at dealerships. The platform leveraged a scalable AWS infrastructure, microservices, and modern DevOps practices to ensure reliability, performance, and operational excellence.

• Architecture and DevOps Leadership for BMW Delivery App
  • Defined and implemented BMW cloud strategy on AWS infrastructure
  • Led cloud architecture and engineering team, ensuring alignment goals
  • Designed TypeScript-based microservices with event-driven, choreography
  • Established Infrastructure as Code (IaC) using AWS CDK for automation
  • Operated CI/CD pipelines, monitoring, alerting, and reporting systems
  • Managed databases (RDS, MongoDB) and optimized performance
  • Scaled and operated Kubernetes platform (EKS) for high availability
  • Drove architecture reviews, quality assurance, and continuous improvement

Applied Methods / Tools

• SAFe / Scrum methodology for agile project management
• Team leadership, mentoring, and knowledge transfer for engineers
• Architecture design and technical reviews for scalable solutions
• Infrastructure as Code with AWS CDK for consistent provisioning
• CI/CD pipeline setup and operations using GitHub Actions
• DevOps and FinOps practices for cost-efficient operations
• Incident management and operational governance for cloud systems
• Workshops, TechTalks, and coaching sessions for knowledge sharing

Applied Technologies

• AWS: Load Balancer, API Gateway, S3, EKS, RDS, MongoDB Atlas
• K8s: TypeScript-based Microservices architecture
• Infrastructure as Code: AWS CDK with TypeScript templates
• CI/CD: GitHub Actions for automation and source management
• Collaboration Tools: Jira, Confluence, Bitbucket for project tracking
• Monitoring & Observability: Metrics, logging, alerts for reliability
• Event-Driven Architecture: Choreography pattern for microservices communication
• Operational Practices: DevOps and FinOps integration for cloud efficiency

At BMW Group, the eSignature application was developed to enable paperless contract signing, supporting the Future Sales Model in BMW eCommerce. The project focused on scalable AWS cloud architecture, security, compliance, and seamless integration with BMW systems.

• Architecture and Requirement Engineering for eSignature Application
  • Defined and implemented BMW cloud strategy on AWS infrastructure
  • Designed and deployed scalable, secure, and compliant cloud architectures
  • Translated business requirements into technical specifications and solutions
  • Led architecture workshops collecting functional and non-functional requirements
  • Coordinated stakeholders across business, product, and IT management
  • Conducted architecture reviews for quality assurance and improvement
  • Implemented CI/CD pipelines and DevOps practices for automation
  • Mentored and onboarded cloud engineers, fostering technical growth

Applied Methods / Tools

• SAFe / Scrum and classic Waterfall for hybrid project management
• Infrastructure as Code (IaC) with AWS CDK for provisioning
• CI/CD pipelines setup and management with GitHub Actions
• DevOps and FinOps practices for cost-efficient operations
• Incident management and monitoring of cloud environments
• Facilitating workshops, TechTalks, and stakeholder alignment sessions
• Architecture design, reviews, and operational validation for cloud systems
• Collaboration with business and IT teams for requirement alignment

Applied Technologies

• AWS: API Gateway, Lambda, Step Functions, S3, EC2
• Cloud Native: Java / Quarkus applications deployed on AWS
• Infrastructure as Code: AWS CDK using Python templates
• CI/CD: GitHub Actions for automation and source management
• Collaboration tools: Jira, Confluence, Bitbucket for project tracking
• Monitoring & Observability: CloudWatch dashboards, alerts, and logging
• Security & Compliance: AWS IAM, policies, and operational controls
• Cloud architecture design following AWS best practices