DevOps Engineer

Qualco Group is a leading Greek technology organisation listed on the Athens Stock Exchange, with a growing footprint in defense, security, civil protection, and critical infrastructure operations. Through its specialised in-house R&D department, the Applied Research & Technology Centre (QART), the Group develops advanced, dual-use solutions and products and provides services powered by Artificial Intelligence, Machine Learning, multi-sensor fusion, and cloud-to-edge architectures. Our mission is to deliver modular, scalable, and mission-ready platforms that support real-time decision-making, autonomous systems, common operational picture and situational awareness, and operational continuity across air, land, sea, and urban environments. Built on robust data architectures and interoperability standards, our solutions integrate with existing systems and evolve alongside operational needs.

We are looking for a Senior DevOps Engineer to strengthen our team by implementing, automating, and maintaining cloud‑native infrastructures, with a focus on Git workflows, MLflow, CI/CD pipelines, and Java application environments. The ideal candidate will contribute to automating infrastructure and ML workflows, ensuring stability, scalability, and high performance. This is a greenfield role with a high degree of ownership: you will define the architecture, tooling, and standards for our cloud-native platform.

Key Responsibilities

• Administration and deployment of containerized Spring Boot Java applications, including performance tuning, monitoring and runtime management;
• Administration of PostgreSQL/PostGIS databases (provisioning, backup/restore, migrations, performance tuning, high availability) and of Apache Kafka clusters (topic/broker management, monitoring, upgrades);
• Management of cloud infrastructure (Azure or other cloud platforms) including provisioning, autoscaling, monitoring, and cost‑efficient operation;
• Management of Git repositories following best practices in branching, versioning, and workflow design;
• MLOps with MLflow including experiment tracking, model registry, versioning, deployment, and integration with Kubernetes;
• Design and implementation of CI/CD pipelines (GitHub Actions, Jenkins, Azure DevOps, ArgoCD) for applications and ML workflows;
• Infrastructure as Code automation using Terraform, Ansible, Helm, and template‑based provisioning;
• Containerization and orchestration with Docker and Kubernetes;
• Monitoring and observability using Prometheus, Grafana, ELK stack, and tracing tools;
• Close collaboration with development, data science, and security teams to achieve zero‑downtime deployments;