DevOps Engineer (Azure)

Role overview A cloud-focused DevOps position within a software team that builds and operates enterprise SaaS solutions. The role centers on owning CI/CD, release automation, and Azure environment operations while partnering with internal stakeholders and external customers on deployment and scaling questions. It suits someone with roughly two to three years of production experience who enjoys a mentorship-driven, remote-first engineering culture.

Responsibilities - Designing, maintaining, and improving continuous integration and continuous delivery pipelines - Managing release processes and driving their automation end to end - Supporting and monitoring development environments and configurations running on Microsoft Azure - Advising internal teams and external customers on installation, sizing, scaling, and CI/CD best practices - Setting up monitoring, alerting, and observability for production-grade cloud workloads - Experimenting with AI coding assistants and LLM-based tools to streamline infrastructure and pipeline work

Requirements - Around 2-3 years of commercial experience configuring, maintaining, and troubleshooting Microsoft-based production systems - Hands-on work with Microsoft Azure Cloud, including ARM templates or Terraform and PowerShell-driven provisioning automation - Familiarity with core Azure services such as App Service, Azure SQL, and Storage, plus an understanding of load balancers, DNS, virtual networks, and firewalls - Working knowledge of Azure Active Directory concepts, including authentication, users, groups, and roles - Solid spoken and written English, with a B.Sc. in Computer Science or a comparable field - Strong communication skills and a collaborative mindset for working across distributed teams

Nice to have - Building and maintaining CI/CD pipelines specifically for .NET projects - Experience with Azure Kubernetes Service, AKS, Service Bus, and Data Factory - Practical use of AI coding assistants such as GitHub Copilot, Claude Code, or OpenAI Codex for scripts, IaC, and pipeline definitions - Familiarity with the Model Context Protocol and how LLM-based tools integrate into engineering workflows - Sound judgment about reviewing and validating AI-generated code before it reaches production

Benefits and work setup - Long-term engagement within a stable, growing SaaS organization - Remote-first, async-friendly environment with a flat structure - Globally distributed team spanning multiple time zones and countries - Culture that emphasizes personal development alongside business results