DevOps Engineer – Kubernetes & Production Operations

We are DXC – a Fortune 500 global IT services leader.

In Bulgaria, we are among the largest IT employers, delivering the full spectrum of the company’s diverse technical portfolio.

Our teams work across cutting-edge technologies, contributing to projects that span industries and geographies.

We foster continuous learning and professional growth, offering access to targeted training and certifications aligned with individual interests and career aspirations. Our culture of collaboration, innovation, and shared values empowers professionals to grow and succeed.

We are looking for an experienced DevOps Engineer to support production environments hosting machine learning and data science applications.

In this role, you will focus on the reliability, stability, and performance of Kubernetes-based infrastructure across Windows and Linux environments, including on-premises systems. You will manage and troubleshoot Kubernetes clusters, support production deployments, implement monitoring and alerting, and automate operational activities.

While exposure to Python and machine learning environments is beneficial, this is primarily an infrastructure, Kubernetes, and production operations role.

• Maintain and support machine learning applications running on Windows and Linux servers in on-premises environments.
• Manage, monitor, and troubleshoot Kubernetes clusters hosting deployed ML/data science workloads.
• Support reliable deployment and operation of models in production environments.
• Implement and maintain monitoring and alerting solutions using DataDog to ensure system health and performance.
• Debug and resolve issues in production environments using monitoring tools and operational best practices.
• Automate operational tasks to improve reliability, scalability, and maintainability.
• Ensure best practices in security, performance, and availability for Kubernetes-hosted applications.
• Document system architecture, deployment processes, and troubleshooting guides.
• Collaborate with engineers and stakeholders to ensure smooth operation of production systems.
  

• Proven experience working with Windows and Linux operating systems in production environments.
• Hands-on experience managing on-premises servers, Kubernetes clusters, and Docker containers.
• Strong understanding of Site Reliability Engineering or DevOps practices.
• Familiarity with monitoring and debugging tools, e.g. DataDog.
• Ability to troubleshoot complex issues in distributed systems.
• Experience with CI/CD pipelines for application deployment.
• Familiarity with AWS cloud platforms.
• Strong problem-solving skills and attention to detail.
• Excellent communication and collaboration skills.

• Preferred Qualifications
  • Experience supporting production machine learning or data science model deployments.
  • Basic understanding of Python for operational support or scripting.
  • Familiarity with OT/IT data bridging, utilizing Ignition to stream robotics and PLC telemetry into cloud environments or enterprise time-series databases (e.g., InfluxDB, Prometheus, AWS Timestream).
  • Familiarity with ML model lifecycle concepts is a plus, but not required.
• The position follows a regular working schedule from 13:00 to 22:00.