Junior DevOps Engineer

Objectives of this position:

Assist with the implementation of DevOps & GitOps philosophies on cloud infrastructure. Work with software developers and engineers to ensure that development follows established processes and works as intended.

What will you do?

• Support the maintenance of Infrastructure as Code (IaC) solutions to automatically and declaratively provision and manage cloud resources.
• Assist in maintaining CI/CD pipelines to automatically analyze, test, build and deploy microservices code to cloud environments.
• Employ GitOps practices to declaratively create Kubernetes clusters, applications, and configurations in multiple environments.
• Maintain and refine centralized monitoring strategy (logging, metrics, and alerting) for cloud resources.
• Monitor workloads on Azure (Log Analytics, Application Insights, Grafana, Prometheus) and perform root cause analysis of technical issues in the deployed applications and infrastructure.
• Support development teams with the daily operations and routine access requests for CI/CD pipelines (CircleCI), code quality gates (SonarQube), and version control (Bitbucket).

What will you need?

• Bachelor of Science in Computer Engineering, IT, IS or equivalent field. IT experience can substitute for a BS degree.
• 1-3 years of commercial IT/DevOps experience. (A minimum of 1 year of hands-on commercial experience is preferred.)
• Foundational knowledge of Linux in physical and virtualized environments, with basic proficiency in Bash scripting.
• Familiarity with at least one declarative Infrastructure as Code (IaC) tool (Terraform, CloudFormation, Azure ARM, OpenTofu, or equivalent).
• Familiarity with at least one corporate oriented CI/CD solution (GitHub, Bitbucket, Gitlab, CircleCI, Azure DevOps orequivalent).
• Familiarity with Agile Software Development, GitOps and Twelve-factor methodology.
• Familiarity with database technologies and popular SQL and non-SQL solutions.
• Familiarity with theinfrastructure concepts required to support Data and AI workloads (e.g., GPU computing basics, AI cloud services).