Identity and Access Management Experts in Munich

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).

• Operations and extensions of Azure DevOps pipelines

• Operations and extensions of AWS services

• Citrix (Windows 10, Bitwarden)

• AWS: ECR, EKS, CloudFront CDN, Route 53, VPC peering and CNI upgrade, Atlas MongoDB, S3 buckets, static website hosting

• Azure: build and deploy with DevOps pipelines

• It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
• GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
• Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
• CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment

• Built Collective Minds as an AI-first company, embedding AI into operating model, delivery and client solutions.
• Built and lead an international offshore delivery organization of 25 professionals, complemented by senior specialists in Germany.
• Act as Interim CDO for ALWITRA, supporting the company-wide digitalization agenda across ERP, CRM, enterprise knowledge, AI, data and operating model.
• Advise managing directors, CIOs and IT leaders on transformation strategy, target operating models, governance, prioritization and execution.
• Own program structures, budgets, staffing, vendor coordination, risks and executive stakeholder communication.
• Develop scalable technology and delivery models across AI, CRM, IAM, integration and manufacturing-related initiatives.

• Spearheaded the EU market entry strategy for L3/L4 autonomous logistics vehicles, driving the technological localization and deployment of the parent company’s smart robotics portfolio.
• Orchestrated technical alignment between top-tier autonomous driving suppliers across China and Europe, translating complex client requirements into precise engineering specifications compliant with EU standards.
• Cultivated strategic joint R&D initiatives with leading European universities, research institutes, and enterprises, accelerating the transition of cutting-edge robotic concepts into commercial products.
• Directed the end-to-end architecture of intelligent warehousing solutions, guiding cross-functional teams in optimizing hardware integration for autonomous vehicles & robots, and overall system performance.
• Led the R&D of high-fidelity simulation and AI algorithms using NVIDIA Isaac Sim & Lab, establishing robust "Sim-to-Real" pipelines to train and validate dynamic path planning optimization, intelligent obstacle avoidance, and complex navigation stacks prior to physical deployment.
• Maintained hands-on oversight of the core system architecture, focusing on bottom-level performance tuning, AI model inference acceleration with TensorRT/ONNX Runtime, and sensor integration.

• Support for implementing the Pimcore CMS, support assistance, and partner management for the existing application landscape.
• Know-how transfer to the existing application landscape.
• Advising stakeholders on the new implementation.
• Support in data modeling for Pimcore.
• Designing and overseeing data migrations from Salesforce to Pimcore using ESB.
• Creating user stories.
• 3rd level support for business-critical processes.
• Technologies: Salesforce Marketing, Sales, and Service Cloud; Talend ESB; Pimcore; Shopware; ERP system Move; DB2; MySQL; MS Office; MS Visio; Jira; Confluence.

• Integrated LangChain middleware between AEM and SAP PIM system
• Developed a FastAPI interface for system communication
• Implemented vector embeddings for semantic product search
• Evaluated LLM models (Vertex AI/Gemini, LM Studio, Hugging Face, OpenAI) for product analysis
• Developed an AEM component to display product recommendations and integrated the recommendation API into the AEM authoring process
• Designed and implemented Pinecone vector database for product embeddings
• Optimized response times and caching strategies
• Evaluated Vertex AI Studio for LLM testing and prompt workflows
• Implemented secure API routing and access control for AI components via FastAPI and gateway validation

• Conducted a comprehensive due diligence review for a potential acquisition of a Swiss manufacturing company as part of a pre-merger analysis
• Assessed production capacities and technical infrastructure
• Evaluated integration possibilities into existing business processes
• Performed risk assessment and developed recommendations for action
• Documented the findings and presented them to management

• 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

Verenburg Consulting GmbH is a specialized IT consulting firm that helps companies plan, manage, and implement complex IT projects. As founder and managing partner, I am responsible for the professional and strategic leadership of the company as well as the operational delivery of client projects.

The role includes leading transformation and digitalization initiatives and introducing sustainable, efficient, and user-focused IT solutions. I work closely with management, business units, and IT organizations to ensure clear decision-making structures, transparent communication, and measurable value.

Responsibilities:

• Strategic consulting on digitalization and modernization of IT and organizational structures
• Taking on the professional and strategic leadership of rollout and transition projects
• Planning, managing, and controlling IT projects using traditional and agile methods
• Building, evolving, and implementing IT service management processes (e.g., ITIL)
• Designing and guiding organizational change processes and introducing new role and process models
• Developing and implementing change management measures to sustainably anchor new ways of working
• Leading, coaching, and developing project teams, including facilitating workshops and management formats

Focus Areas & Expertise:

• Rollout and transition management for international and national IT deployments
• IT service management (service transition / service operation / process design)
• Project management (traditional and agile, e.g., Scrum, SAFe, Prince2)
• Organizational development and transformation of operational and service models
• Change management based on recognized models (e.g., ADKAR)
• Strategic digitalization consulting, including requirements analysis and process/system evaluation

Working Style: My approach is highly value-driven, structured, and solution-focused. I combine technical understanding with a business perspective to ensure sound decisions, efficient processes, and sustainable results. Clear communication, transparency, and close coordination with all stakeholders are always at the center.

• Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).

• Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.

• Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.

• CMP also includes the complete hardware management lifecycle.

• Kubernetes, OpenStack and Hadoop are used as the foundation.

• The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.

• Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.

• Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.

• OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.

• Development of a Java application Rudi: SOAP, REST, containers, DB.

• Technologies: Kubernetes (K3s, Rke2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3S), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).

• Responsible for tool-based project and portfolio management in the group
• Designing, enhancing and operating Jira- and Confluence-based workflows
• Translating management and business requirements into reliable tool concepts
• Integrating Jira into existing tool landscapes (e.g. Planview, ServiceNow, SAP BW)
• Establishing governance, reporting and KPI structures
• Rolling out new workflows including training & key user models
• Using AI features to support planning & analysis