Software Engineer -2

Microsoft Microsoft · Big Tech · Hyderabad, TS, IN · Software Engineering

Software Engineer role in Microsoft's CoreAI division, focusing on building AI-powered developer tools and services. The role involves designing, building, and operating core platform services for developers across the application lifecycle, including code management, CI/CD, testing, and running applications on Azure PaaS. It emphasizes AI as a core building block, integrating AI into engineering and lifecycle systems to improve efficiency, quality, and productivity, while ensuring enterprise-grade trust, security, and reliability.

What you'd actually do

  1. 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.
  2. 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.
  3. AI-enabled engineering and lifecycle systems that improve testing efficiency, quality analysis, incident triage, and developer productivity across the software development lifecycle.

Skills

Required

  • 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.

What the JD emphasized

  • enterprise trust
  • security
  • reliability
  • responsible AI practices

Other signals

  • AI-powered developer tools
  • planet-scale platforms
  • enterprise trust, security, and reliability
  • AI as a core building block
  • AI-enabled engineering and lifecycle systems