Principal Software Engineering-coreai

Microsoft Microsoft · Big Tech · Redmond, WA +1 · Software Engineering

Principal Software Engineer at Microsoft Foundry, Core AI, focused on building and scaling the platform for intelligent agents and generative AI systems. This role involves driving technical direction, architectural decisions, and ensuring quality, reliability, security, and compliance for large-scale AI systems. The engineer will mentor others, influence without authority, and champion automation and responsible AI practices.

What you'd actually do

  1. Drives the improvement of artificial intelligence (AI) tools and practices across the software development lifecycle (SDLC).
  2. Provides technical leadership during code reviews for a solution/product area to assure it meets team standards, contains the correct test coverage, and is appropriate for the product or solution area.
  3. Establishes best practices and mentors others to create a clear test strategy that ensures solution quality, prevents regression from being introduced into existing code.
  4. Designs and executes plans for redesigning or rearchitecting difficult or untestable sections of code across solutions and/or products.
  5. Leverages artificial intelligence (AI) tools for test automation.

Skills

Required

  • Software development lifecycle (SDLC)
  • Architectural decisions
  • Large-scale systems
  • AI technologies
  • Quality, reliability, security, and compliance
  • Customer and developer needs analysis
  • Technical leadership
  • Mentoring engineers
  • Influencing without authority
  • Design principles
  • Code reviews
  • Continuous learning
  • Collaboration with partner teams
  • Automation
  • Operational excellence
  • Secure-by-design practices
  • Responsible AI practices
  • AI tools and practices
  • Coding standards
  • Automated source code analysis
  • Extensible, maintainable, well-tested, secure, and performant code
  • Code performance optimization
  • Debugging tools
  • Testing frameworks
  • Incident retrospectives
  • Least-access principles
  • Privacy and security
  • Test strategy
  • Security testing
  • Test automation
  • System architecture

Nice to have

  • Generative AI (GenAI)

What the JD emphasized

  • critical role in building and evolving the platform that enables developers and enterprises to design, deploy, and scale intelligent agents and generative AI systems
  • owning architectural decisions for complex, large‑scale systems that integrate cutting‑edge AI technologies
  • highest standards of quality, reliability, security, and compliance
  • technical leadership across teams
  • accelerating value to customers
  • raise the engineering bar through strong design principles, rigorous code reviews, and a culture of continuous learning
  • seamless integration, scalable architectures, and robust deployment and testing frameworks
  • champion automation, operational excellence, and secure‑by‑design practices
  • define how AI systems and agent platforms are built responsibly and at scale
  • shaping how the world interacts with intelligent systems
  • Incorporates Responsible AI practices into the SDLC
  • Experiments with AI tools and practices to improve their own capabilities
  • Leads by example across teams and mentors others to produce extensible, maintainable, well-tested, secure, and performant code used across the company that adheres to design specifications.
  • Leads efforts to continuously improve code performance, testability, maintainability, effectiveness, and cost, while accounting for and incorporating relevant trade-offs.
  • Creates and applies metrics to drive code quality and stability, appropriate coding patterns, and best practices.
  • Leads efforts to identify and anticipate blockers or unknowns during the development process, escalate them, and communicate how they will impact timelines, and then drives the identification and implementation of strategies and/or opportunities to address them.
  • Acts as an expert on using debugging tools, tests, logs, telemetry, and other methods, and proactively leads verification of assumptions through while developing code before issues occur across products and teams in production.
  • Leverages minimal telemetry data, triangulates issues, and resolves with minimal iterations.
  • Leads incident retrospectives to identify root causes of problems, and owns the implementation of repair actions and the identification of mechanisms to prevent incident recurrence.
  • Drives applying least-access principles, using logging, telemetry, and other appropriate mechanisms to investigate issues while retaining privacy and security, and champions those practices across the team.
  • Establishes best practices and mentors others to create a clear test strategy that ensures solution quality, prevents regression from being introduced into existing code.
  • Establishes best practices and mentors others on ensuring test plans incorporate security testing to validate security invariants (including negative cases).
  • Provides technical leadership on adding new tests to cover gaps, deleting or fixing broken tests, and improving the speed, reliability, and defect localization of the overall test suite across a solution or product.
  • Mentors others on, and builds testable code and considers testability during design across solutions and/or products.
  • Acts as a thought leader for understanding different types of tests that can be done on a particular system (e.g., unit tests), and maintaining up-to-date understanding of testing architectures used both across Microsoft and across the industry, and applies them across the architecture as appropriate.
  • Designs and executes plans for redesigning or rearchitecting difficult or untestable sections of code across solutions and/or products.
  • Leverages artificial intelligence (AI) tools for test automation.
  • Oversees, influences, and owns efforts and design discussions for the overall system architecture of entire pr

Other signals

  • building and evolving the platform that enables developers and enterprises to design, deploy, and scale intelligent agents and generative AI systems
  • drive technical direction across the full software development lifecycle, owning architectural decisions for complex, large‑scale systems that integrate cutting‑edge AI technologies
  • champion automation, operational excellence, and secure‑by‑design practices, helping define how AI systems and agent platforms are built responsibly and at scale