Daniel Bovan

MLOPS/ DevOPs for AI Use Cases. Deployment and Development of AI Applications with Compliance Rules like Infrastructure as Code OIDC Security Sonar etc

Close cooperation with development teams to implement business use cases for banking applications

Optimizing configuration management and improving Terraform deployments.

Improving development and testing workflows using GitHub Actions.

Scaling AI use cases from POC to ensure compliance with banking processes, secure and conform deployment, and thorough security checks.

During the project, I was responsible for deploying and testing the backend of a software product in OpenShift. I containerised the product and developed an end-to-end build and test process using Shell, Jenkins, GitLab CI and Maven. The deployment was automated in various Kubernetes clusters using Jenkins, Maven and GitLab CI. For quality assurance, I implemented test automation with SoapUI, Maven, JUnit and Citrus and set up test environments using Docker, Kubernetes and Vagrant. I also used tools such as SonarQube for static code analysis and Syft and Mend for security analyses. I established an automated development process and took over the management of infrastructure components such as Jenkins, GitLab, SonarQube, Nexus, Proxmox and Kubernetes. I also worked as a DevOps team lead using agile methods such as Scrum and Kanban.