Job description

Role Overview

We are looking for a proactive and curious DevOps Engineer who is passionate about automation, cloud infrastructure, software development, Kubernetes, and artificial intelligence.

The ideal candidate follows developments in the AI industry, experiments with new tools, and works on personal projects to continuously improve their technical skills. This role is suited to someone who enjoys solving problems, taking ownership, learning independently, and challenging themselves to exceed their current capabilities.

Build, maintain, and improve reliable and scalable cloud infrastructure.

Deploy, manage, and troubleshoot Kubernetes clusters and containerized applications.

Monitor cluster health, performance, availability, and resource usage.

Manage Kubernetes workloads, services, ingress, storage, networking, secrets, and configuration.

Develop and maintain CI/CD pipelines for automated software delivery.

Automate infrastructure provisioning, configuration, monitoring, and operational processes.

Manage infrastructure and services primarily on AWS.

Support selected workloads and services on Microsoft Azure.

Troubleshoot infrastructure, application, deployment, networking, and cluster-related issues.

Work closely with software engineers to improve development and release processes.

Use Infrastructure as Code tools to create repeatable and scalable environments.

Identify opportunities to use AI tools to improve engineering productivity, automation, monitoring, and internal workflows.

Research new technologies and recommend practical improvements.

Document systems, architecture, processes, and operational procedures.

Professional experience in DevOps, cloud engineering, software engineering, systems engineering, or a related role.

Strong practical experience with Kubernetes and production cluster management.

Experience deploying, scaling, upgrading, monitoring, and troubleshooting Kubernetes clusters.

Experience with managed Kubernetes services, preferably Amazon EKS.

Familiarity with Azure Kubernetes Service is an advantage.

Strong understanding of Docker and containerized environments.

Practical experience with AWS services and cloud architecture.

Familiarity with Microsoft Azure services and concepts.

Strong understanding of software development principles and the software delivery lifecycle.

Experience with at least one programming or scripting language, such as Python, JavaScript, Bash, Go, or PowerShell.

Experience with CI/CD platforms and automated deployment processes.

Knowledge of Infrastructure as Code tools, such as Terraform, AWS CloudFormation, or Azure Bicep.

Understanding of Linux, networking, security, monitoring, logging, and troubleshooting.

Experience using Git and modern source control workflows.

The successful candidate should have experience with:

Kubernetes architecture, including control plane and worker node components.

Cluster installation, configuration, maintenance, and upgrades.

Deployments, StatefulSets, DaemonSets, Jobs, and CronJobs.

Services, ingress controllers, load balancing, and DNS.

Kubernetes networking, storage, secrets, and configuration management.

Helm charts and Kubernetes package management.

Autoscaling, resource limits, requests, and capacity planning.

Role-Based Access Control, identity, permissions, and cluster security.

Cluster monitoring, logging, alerting, and incident troubleshooting.

High availability, disaster recovery, backup, and restoration processes.

Production environments with multiple clusters or namespaces.

We are particularly interested in candidates who:

Actively follow developments in AI, large language models, automation, and developer tools.

Experiment with AI tools in their work or personal projects.

Understand how AI can support engineering, operations, monitoring, and automation.

Build personal projects, contribute to open-source projects, or independently explore new technologies.

Demonstrate a strong desire to learn, improve, and expand beyond their current capabilities.

Proactive and comfortable taking ownership.

Curious, self-motivated, and willing to experiment.

Able to identify problems and propose solutions without waiting for detailed instructions.

Strong analytical and problem-solving skills.

Comfortable working independently and collaborating with technical teams.

Open to feedback and committed to continuous improvement.

Ambitious and motivated to exceed their current level of knowledge and performance.

Experience with Amazon EKS, Azure Kubernetes Service, or self-managed Kubernetes clusters.

Experience with GitOps tools such as Argo CD or Flux.

Experience with service mesh technologies such as Istio or Linkerd.

Experience with observability tools such as Prometheus, Grafana, Datadog, CloudWatch, or Azure Monitor.

Knowledge of cloud security, identity management, and access control.

Experience integrating AI APIs or building AI-powered tools.

Public personal projects, a GitHub portfolio, technical writing, or open-source contributions.

Relevant AWS, Azure, Kubernetes, or DevOps certifications.

The opportunity to work with modern cloud, Kubernetes, automation, software, and AI technologies.

A role with meaningful ownership and room to introduce new ideas.

Support for professional development and continuous learning.

The freedom to experiment, improve processes, and build better technical solutions.

A collaborative environment for people who want to challenge themselves and grow.