Software Engineer II - Ctj- Poly

Microsoft Microsoft · Big Tech · Redmond, WA +3 · Software Engineering

Software Engineer II role focused on building and managing hyperscale infrastructure and capacity solutions across multiple clouds, supporting both public Azure and government clouds. The role involves full-stack, with an emphasis on backend development, to manage hardware lifecycle and ensure workloads can land on new capacity. It also includes AI-native development practices within the software development lifecycle.

What you'd actually do

  1. Uses appropriate artificial intelligence (AI) tools and practices across the software development lifecycle (SDLC) in a disciplined manner. Takes responsibility for the content of their AI-generated changes to artifacts, reviewing all changes and applying appropriate tooling and processes with minimal guidance.
  2. Supports efforts to use debugging, tests, tools, logs, telemetry, and other methods to proactively verify assumptions before issues occur for product features in production. Conducts incident retrospectives to identify root causes of problems, implements repair actions, and identifies mechanisms to prevent incident recurrence with minimal supervision.
  3. Reviews product feature code and test code to ensure it meets team standards, contains the correct test coverage, and is appropriate for the product feature. Contributes to bringing insight to code reviews to help improve code quality, coaching and providing feedback to develop other engineers' skills with minimal guidance.
  4. Creates and implements code for a product, service, or feature, reusing code as applicable with minimal supervision. Writes and learns to create code that is extensible and maintainable.
  5. Understands and provides feedback for proposals for architecture, with technical leadership from others. With minimal supervision, tests and explores various design options for a product/solution feature, outlining strengths and weaknesses of each option.

Skills

Required

  • Full-stack development
  • Backend engineering
  • Cloud infrastructure management
  • Capacity planning
  • Software development lifecycle (SDLC)
  • Debugging
  • Testing
  • Logging
  • Telemetry
  • Incident retrospectives
  • Code reviews
  • Code quality
  • Extensible and maintainable code
  • Architectural design
  • Test strategy
  • Security testing
  • Privacy
  • Compliance

Nice to have

  • AI-generated changes review
  • Least-access principles
  • Automated source code analysis tools
  • Generative AI (GenAI) for coding
  • Performance
  • Scalability
  • Resiliency
  • Cost of Goods Sold (COGS)

What the JD emphasized

  • hyperscale solutions
  • manage and scale out infrastructure and capacity
  • fully automated fashion
  • AI to supercomputing
  • national security mission
  • AI tools and practices