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Do you want to build AI-powered developer tools and services that enable a billion builders to bring their ideas to life every day? Are you excited to build planet-scale platforms with enterprise trust, security, and reliability baked in from design to delivery? If so, this role in Microsoft’s CoreAI division is for you. As a Software Engineer in our Developer Platforms team, you will design, build, and operate core platform services for developers across the entire application lifecycle. We build products and services for every need—from code management and CI/CD with Azure DevOps, to testing applications at scale with Azure App Testing, to running applications on Azure PaaS platforms such as Azure App Service, Container Apps, Functions, API Management, and Logic Apps. We also build Azure- & Foundry-native integrations with leading technology providers (for example, MongoDB, Elastic, and Datadog) to give customers flexibility, choice, and confidence as they build and operate modern cloud applications—using AI as a core building block, not an afterthought.
Job Responsibility:
Build AI-powered cloud services and platforms that support developer and enterprise workflows, owning them end-to-end—from architecture, design and implementation to deployment and live-site operations
Build cloud-to-edge platform capabilities, including Azure resource providers, data-plane integrations, and portal experiences that enable secure, scalable management of modern applications and AI workloads
Build AI-enabled engineering and lifecycle systems that improve testing efficiency, quality analysis, incident triage, and developer productivity across the software development lifecycle
Requirements:
Bachelor’s or Master’s degree in Computer Science, or equivalent practical experience
3-7 years of experience building production software using one or more modern programming languages such as C#, C++, Go, Java or Python
Strong understanding of software engineering fundamentals, data structures, and problem-solving
Ability to learn new technologies quickly and adapt to deliver customer and business impact
Nice to have:
Experience working in Linux environments and with open-source projects
Familiarity with containers and orchestration technologies such as Docker and Kubernetes
Experience with cloud infrastructure (Azure, AWS, or equivalent)
Exposure to site reliability engineering (SRE) practices
Exposure to AI-assisted development and data-driven engineering workflows
Knowledge of Azure resource providers, platform extensibility, and security, compliance, or responsible AI concepts