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• Designed and developed the architecture of an enterprise SCM/CI platform for Kubernetes-native delivery and GitOps workflows.
• Implemented infrastructure automation and Vault & IAM integration for secure, compliant pipelines.
• Coordinated cross-functional teams to improve DevOps, security, and architecture in release processes.
• Increased platform adoption and developer experience by automating onboarding and artifact pipelines.

• Porsche ID is a unified digital identity platform providing secure authentication and seamless access across Porsche’s online services, mobile apps, and connected vehicle features
• Designed and implemented new authentication and authorization functionalities for both users and systems
• Ensured high availability, security, and performance to deliver a flawless digital experience for Porsche customers
• Technologies: Auth0, Angular, Tailwind, AWS, Terraform, Github
• Methodologies: Scrum and SAFe

• Planning and implementing the upgrade of the existing TYPO3 installation to TYPO3 11, including switching to a Composer setup (Composer-based deployment)
• Migrating the underlying MySQL database

• Infrastructure management with Puppet and Terraform for consistent environments
• Maintenance and adaptation of Terraform scripts for Azure cloud deployment
• Migration of database systems and services to the Azure cloud
• Introduction of GitOps with ArgoCD for fully automated deployments
• Adaptation and development of GitHub pipelines for CI/CD workflows
• Creation of Helm Charts for standardized deployments
• Migration of repositories from Bitbucket to GitHub
• Operation and performance optimization of an Oracle 19c grid cluster (RAC, Dataguard)
• Setup and management of MongoDB instances (on-premise and Azure Kubernetes)
• Setup of PostgreSQL clusters in on-premise and Azure Kubernetes environments
• Migration of services, master data, and stored procedures from Oracle to PostgreSQL
• Troubleshooting and performance tuning of complex data infrastructures
• Installation, configuration, and upgrade of Tableau in the productive BI environment
• Development of sanity checks to monitor business processes and application logic
• Adaptation of the backup and recovery strategy to new requirements
• Carrying out disaster recovery and point-in-time recovery
• Technologies used: Oracle 19c RAC Grid Dataguard, PostgreSQL 16, MongoDB, MySQL Cluster, Kubernetes (Azure), Tableau, Puppet, Terraform, ArgoCD, GitHub/Bitbucket, CheckMK, Prometheus, Grafana, Icinga2, Ubuntu/RHEL

SCS provides its services to various trading and settlement locations and offers a variety of functionalities related to settlement. The business objectives of the C7 SCS are:

• Safeguard against counterparty risk.
• Reduction of External Settlement Volume.
• Central Risk Management.
• Post-Trade Anonymity.
• Efficient Failure Management.

Technologies: Java 17, Spring Boot 3, Microservices (REST API), Spring Data JPA/Hibernate, Sonar Qube, fortify findings, Mockito, Jenkins, Docker RHEL-OpenShift, Maven, Pod man, GitHub, JIRA, WIKI, Liquibase, Swagger API, AMQP, Apache Camel, GCP Machines Infra setup, Terraform, PostgreSQL, React.js, Instana, Graylog, Daisy LLM Usage, GitHub Copilot.

Roles and Responsibilities:

• Followed SOLID Design Principles for Microservice implementation.
• Reviewed and refactored codes were appropriate in line with best practices, accessibility, security, quality, and performance.
• Developed applications using build tools, Maven, and continuous integration using Jenkins.
• Followed the CI/CD process and ensured that the developed code passed the Sonar Qube quality gate standards.
• Used DBAG Artifactory Repository Manager for updated application images, versions, and jar files.
• Deployed the microservices using the container system Padman and container orchestration tools like OpenShift.
• Used Liquibase for the database version control system.
• Enhanced application performance by optimizing Java code and implementing efficient algorithms.
• Experienced in identifying and remediating known CVEs in third-party libraries using SCA tools to ensure secure and compliant security Clearing Services components, Fixed the issues in SCS components.
• Experienced in vulnerability remediation, including identifying security weaknesses, prioritizing risks, and implementing effective fixes to strengthen SCS System.
• Enabled mTLS for database authentication and broker setups to enforce encrypted, certificate-validated communications.

• Built and operated an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, and Autoformer).
• Implemented CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform) and data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) to training and evaluation, model registry, and endpoint deployment.
• Integrated MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
• Developed and containerized 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), centralized logging, and cost monitoring.
• Automated infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connected to existing market data systems and event pipelines.
• Migrated existing workloads and databases (IONOS → Azure, MongoDB) and integrated them into central MLOps workflows and internal networks.
• Extended 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.
• Analyzed and designed a software solution to efficiently process large volumes of data (>3000 messages/sec) (market data store).
• Developed Spring Boot / Java 21 container services with RabbitMQ to distribute exchange data through 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.
• Integrated RESTHeart to create a REST API for MongoDB.
• Built 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.
• Developed Python scripts to transform and clean incoming exchange data (Pandas, scikit-learn).

• Adjusting, optimizing, configuring, and administering a multi-stage GitLab instance with over 250 users
• Building, adjusting, expanding, and optimizing infrastructure, configuration, and monitoring
• Providing services and handing them over to production
• System environment: DependencyTrack, GitLab, Grafana, Hedgedoc, Kubernetes, OAuth2 Proxy, Openstack, Prometheus, Syseleven

• In this role, I was responsible as Lead Software Engineer for automating various meat production lines.
• My focus was on developing highly available (High Availability) systems using C/C++.
• I also implemented and supported container orchestration with Kubernetes to ensure smooth and scalable production operations.

• Identification of requirements (legal, organizational, and technical)
• Design of solution architectures
• Evaluation of concepts and technologies
• Preparation of decision templates
• Architectural Decision Records (ADR)
• Coordination of implementation
• Review of implementations
• Documentation

• Defining the software architecture
• Designing and developing modules
• Assisting internal staff with learning and onboarding
• Tools: SAP CAP / CDS, Java 21, SAP HANA, SAP Business Technology Platform (BTP)

• IaC & Automation: Designed and automated provisioning of about 40 Linux servers and cloud resources using Terraform and Ansible (reducing manual effort by over 80%).
• Container Orchestration: Built and operated two production Kubernetes clusters with 70+ Docker containers to ensure high availability, scalability, and self-healing.
• CI/CD Optimization: Developed and maintained 15+ CI/CD pipelines with Jenkins and GitHub Actions (shortening deployment times from several hours to under 15 minutes).
• OS Hardening & Operations: Automated patch management, OS configuration, and security hardening for 40+ Linux servers with Ansible to improve compliance.
• Monitoring & Alerting: Implemented centralized monitoring and alerting solutions for proactive issue detection and minimized system downtime.
• Cross-Functional Collaboration: Worked across Dev, Ops, and Security teams to establish DevOps best practices and infrastructure-as-code standards.

• Implementation of specified DevOps solutions to automate infrastructure (Terraform, Bicep, CloudFormation, Ansible) on-premises datacenter (Ovirt, Proxmox, Ceph Cluster, MinIO) and private cloud.
• Administration, configuration and implementation of CI/CD DevOps pipelines (GitLab, GitFlow) to support development process (Artifactory, Prometheus, Istio, service mesh, Helm Chart, OpenShift (Red Hat Enterprise) / Kubernetes cluster), Red Hat Satellite.
• Administration, setup, monitoring and patching of Linux infrastructure based on Red Hat Enterprise for Dev, Test and QA.
• Use of Scrum and Kanban methods.
• Administration, configuration and implementation of security standards for deploying on Dev, Test, QA and Prod stages of the new ePA applications.
• Development of new plugins and add-ons needed on current infrastructure.
• Database support.
• Data analytics support (Python, Spark, Pandas, Power BI, Splunk Enterprise).
• Implementation of best practices for DevSecOps and BizDevOps using GitOps (ArgoCD), Streamlit framework, Semaphore Ansible UI.
• Configuration and testing of iperf, uperf, sysbench using benchmark-operator for external source data and IoT/MDM devices, creating reports via ELK / OpenSearch.
• Building a new Databricks platform to collect and analyze big data from different sources and IoT devices into Hadoop framework (Python, Pandas, PySpark, Power BI, Apache Airflow).
• Building backend data aggregation and processing to automate configuration deployment between different OpenShift clusters and big data framework (Python, Pandas, PySpark, Apache Spark, PostgreSQL, Django 2, Ansible Automation, Jira JSM).
• Building a new ML pipeline platform using Kubeflow, TensorFlow, KServe.
• Data extraction, transformation and loading from different data sources including structured and unstructured data to analytic DWH / big data cluster using Python, Pandas, Polars, Power BI, Django backend and PostgreSQL.
• Setup of new DevOps Test and QA HashiCorp Vault cluster for PKI and IAM.
• Configuration and testing of automated patching based on CVSS score, SIEM-integrated CVEs.
• Use of Nexpose and InsightVM to scan vulnerability events in network, host, container and application.
• Design and implementation of secure and scalable AWS architectures including VPC, EC2, S3, RDS and Route53 and similar setups on Azure and GCP.
• Automated system provisioning and deployment using CloudFormation templates.
• Configuration of IAM roles, policies and permissions to ensure secure access control.
• Patch management, backup automation and disaster recovery setup on AWS infrastructure.
• Monitoring and optimization of system performance using AWS CloudWatch and AWS Trusted Advisor.
• Support of VMware services (vSphere, Aria, Horizon) and the virtual desktop environment.
• Development and maintenance of CI/CD pipelines using Jenkins, GitLab CI/CD and AWS CodePipeline with interface to Nutanix.
• Configuration of AWS CloudWatch to monitor application performance and system events.
• Planning and execution of migration of on-premises applications to AWS cloud platforms.
• Deployment of containerized applications using Docker and Kubernetes in AWS environments.
• Deployment of internal software packages between availability zones using AWS CodeDeploy.
• Building and deploying ML models using Scikit-learn, XGBoost and Spark MLlib including hyperparameter tuning, model evaluation and production deployment.

• Technical leadership and architecture for enterprise clients (including LR Health & Beauty, Deutsche Bahn, Trusted Shops)
• Cloud migration, microservices architectures, CI/CD optimization

• Migration and maintenance of a legacy system to the latest version of .NET for the asset management system
• Design and implementation of tests
• Creation of deployment pipelines and documentation
• Further development and implementation of change requests
• Technologies: C#, ASP.NET, Unit of Work, IOC, Three tier architecture, Entity Framework Core, DB First, Web API, Swagger, Open API, Monolith, Python, Oracle, Azure DevOps, GIT, xUnit, NSubstitute
• Methods: Agile development using Scrum