Junior DevOps Engineer
Salary: $5,000 – $6,000 per month
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).