DevOps Engineer – Cloud & MLOps (QART)

About the role

We are looking for a DevOps Engineer to join the Applied Research & Technology Centre (QART) of Qualco Group, a leading Greek technology organisation operating in defence, security and critical infrastructure. The role will focus on designing, automating and maintaining cloud‑native infrastructures, supporting Java/Tomcat applications and end‑to‑end machine‑learning workflows.

Key responsibilities

• Administer Java/Tomcat environments, including clustering, performance tuning, deployments and containerised Java application management.
• Manage cloud infrastructure on Azure (or other platforms), handling provisioning, autoscaling, monitoring and cost‑efficient operation.
• Implement GitOps workflows, maintain repositories and enforce best‑practice branching and versioning.
• Develop MLOps pipelines with MLflow for experiment tracking, model registry, versioning, deployment and Kubernetes integration.
• Design and maintain CI/CD pipelines using GitHub Actions, Jenkins, Azure DevOps or ArgoCD for both application and ML workflows.
• Automate infrastructure as code with Terraform, Ansible, Helm and template‑based provisioning.
• Containerise workloads with Docker and orchestrate them on Kubernetes.
• Set up monitoring and observability using Prometheus, Grafana, ELK stack and tracing tools.
• Collaborate with development, data‑science and security teams to achieve zero‑downtime deployments and ensure regulatory compliance.

Required profile

• Bachelor’s degree in Computer Science, Engineering or a related field (required); Master’s degree is a plus.
• Minimum 3 years of experience in DevOps roles with a strong focus on automation and cloud‑native environments.
• Proven experience administering Java/Tomcat, containerising Java applications and managing Tomcat clusters.
• Hands‑on experience with GitOps, CI/CD tools and MLOps platforms such as MLflow or Kubeflow.

Required skills

• Java, Tomcat
• Azure (or other public cloud platforms)
• Git, GitHub Actions, Jenkins, Azure DevOps, ArgoCD
• MLflow, Kubeflow (or similar MLOps tools)
• Docker, Kubernetes
• Terraform, Ansible, Helm
• Prometheus, Grafana, ELK stack