Principal Software Engineering Architect

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

This role focuses on architecting and developing core network management and operations for a large enterprise network, leveraging AI for operations, intelligent automation, and agent-driven workflows to enable autonomous operations and a 'Frontier Firm' model. The role involves defining requirements, leading design, mentoring on AI-assisted development, and taking ownership of services with a focus on AI-driven observability and self-healing systems.

What you'd actually do

  1. Partner with stakeholders to define user requirements across key scenarios, with an emphasis on AI-driven operations, intelligent automation, and agent-enabled user experiences.
  2. Lead the identification of dependencies and drive the development of design documents for a product, application, service, or platform, incorporating AI-first and agentic architectures that enable autonomous operations and continuous optimization.
  3. Mentor others to write and review high-quality, maintainable, and extensible code, while embedding AI-assisted development practices and enabling engineers to effectively leverage copilots and intelligent agents.
  4. Collaborate with cross-functional teams to drive project plans, release plans, and execution, integrating AI-powered insights and agent-driven workflows to accelerate delivery and improve decision making.
  5. Take end-to-end ownership of services as a Designated Responsible Individual (DRI), including on-call responsibilities, while advancing autonomous operations through agent-based monitoring, incident detection, and response to improve reliability and resilience

Skills

Required

  • Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python

Nice to have

  • 5+ years of experience designing and operating large-scale enterprise services, including production systems.
  • Experience building and operating large-scale infrastructure and network management systems.
  • Experience with Infrastructure as Code (IaC) tools (e.g., Terraform, ARM, CloudFormation) to automate deployment and configuration.
  • Experience designing resilient, secure, and highly available architectures in cloud or hybrid environments.
  • Experience applying AI/ML or generative AI technologies (e.g., LLMs) to real-world engineering problems.
  • Experience building solutions from concept to production.
  • Experience improving monitoring, observability, and incident response for mission-critical systems.

What the JD emphasized

  • AI-driven operations
  • agent-enabled user experiences
  • AI-first and agentic architectures
  • autonomous operations
  • agent-driven workflows
  • AI-powered insights
  • agent-based monitoring
  • AI-driven observability
  • predictive insights
  • self-healing systems

Other signals

  • AI-powered operations
  • agent-driven workflows
  • autonomous operations
  • intelligent automation