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• Onboarded and conducted comprehensive documentation and system analysis to assess the existing data infrastructure, facilitating rapid integration and collaboration across functional data teams (modelling, processing, reporting).
• Collaboratively defined the architecture and project structure for a central data pipeline repository, including hierarchical standards, knowledge management strategies, and role-specific responsibilities, enhancing maintainability and onboarding speed.
• Evaluated and validated open-source data routing tools (Airbyte, Apache NiFi, Dragster) for ingest and sync requirements in retail analytics, including local benchmarking and error-state testing.
• Led the design and deployment of Airbyte in Kubernetes, creating customized Helm charts, securing secrets handling, and configuring Ingress with TLS and internal DNS routing, ensuring full API and UI accessibility.
• Troubleshot and resolved Ingress controller issues, iterating through multiple stages of debugging and testing, and documented setup and replication steps for scalable reuse.
• Mapped data models to ARTS standard, supporting schema alignment for ERP and reporting use cases, and coordinated review loops to align future data processing logic.
• Drafted strategic 1-pagers comparing MinIO, Pub/Sub, and routing architectures, providing technical guidance for architectural decisions and investment planning.
• Enabled secure access and authentication mechanisms, including initial evaluation for SAML integration, cluster-level configuration reviews, and service annotation improvements.

• 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

• 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).

• 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.

• 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.

• Promotes continuous integration and delivery (CI/CD) by applying DevOps principles to accelerate and automate development and operations processes.
• Provides hands-on support in implementing and deploying IaC using Bicep.
• Manages and optimizes cloud infrastructure on Microsoft Azure, ensuring scalability, availability, and efficiency.
• Implements and monitors comprehensive security measures, especially in network and data security, to protect systems and data.
• Manages and operates Kubernetes clusters to enable efficient orchestration and scaling of containerized applications.

• Bootstrapping a CloudOps team and building a multi-cloud provider backend for a low-code Internal Developer Platform (IDP) with env zero

• Introducing user story mapping, ADRs, milestones, and backlog management

• Designing and developing core APIs, setting up CI/CD pipelines, OpenTofu/Terraform scripts

• Representing and communicating the team with third-party stakeholders (e.g. env zero)

• (Cross-)team coaching on DevOps, software design, Terraform, Golang, and agile practices

• Provision Kubernetes infrastructure on Azure, GCP, or bare-metal at the hosting provider
• Design and implementation of REST and GraphQL APIs
• Cloud governance (cost, compliance, security)
• Infrastructure-as-Code and CI/CD as code (Terraform, Ansible)
• Container technologies (Docker, Helm)
• Maintain and improve CI/CD pipelines (GitLab CI, GitHub Actions), Argo CD
• Deployment, scaling, and monitoring of microservices
• Distributed event streaming (Kafka)
• Message brokers RabbitMQ, MQTT
• Software development from scratch or further development of existing projects using PHP, Python, Go, C#, and Bash

• Development of an application for process optimisation in productive operation in an air-gapped environment
• Connecting four different systems with interfaces: REST, SMEMA, MQTT, COM
• Technologies: Linux, On-Prem, Docker, Python, Bash Scripting

• Building and scaling cloud infrastructure on GCP to support a SaaS platform for industrial clients
• Designing and implementing a data-driven DevOps pipeline for streamlined deployment and CI/CD workflows
• Collaborating with Data Science team on MLOps workflow to automate integrated retraining

• Extension of OSGi modules in a complex communication solution
• Debugging communication with Wireshark and remotely in the JENS test environment
• Migration of tests from TestNG to JUnit 5 with Mockito and AssertJ
• Updating vulnerable dependencies and improving code quality
• Changes to the Vaadin frontend
• Increasing test coverage