Principal AI Software Engineer - DevOps Acceleration

Note: The job is a remote job and is open to candidates in USA. Microsoft is seeking a Principal Software Engineer to use AI and automation across DevOps to reduce the time from idea to launched feature. The role sets engineering principles and guardrails for AI adoption, evaluates industry tools, measures outcomes, and guides technical direction across Microsoft Commerce.

Responsibilities

• Shorten the path to launch. Map the full lifecycle from idea to production, find the biggest delays, and remove them with AI and automation
• Put AI into every stage of DevOps: planning, coding, code review, testing, release, rollout and incident response. Move what works from pilot to the default way of working
• Set principles with clear opinions. Define global design principles for using AI in engineering, including where it should and shouldn't be used, and make sure teams adopt them
• Build in guardrails. Speed must not cost us security, compliance, Responsible AI or reliability. Deliver this through automated controls, not manual approvals
• Bring the industry's view. Continuously evaluate AI tools and practices across the industry, make clear build, buy or skip recommendations, and keep Commerce Engineering ahead of the curve
• Be the critical voice. Challenge designs, roadmaps and AI claims with evidence, so our offerings get better instead of just looking busy
• Measure what matters. Track progress with metrics such as lead time, deployment frequency, change failure rate and developer experience
• Lead through influence. Mentor senior engineers, lead architecture reviews, and set technical direction across Microsoft Commerce
• Embody our [culture]( "") and [values]( "")

Skills

• Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, Go, Rust, C, C++, C#, Java, JavaScript, or Python OR equivalent experience
• A proven record of using AI tools or agentic workflows to measurably shorten engineering cycle time at scale
• Deep, hands-on knowledge of the AI developer tools landscape, with well-reasoned opinions on what works
• Demonstrated DevOps and SRE background: CI/CD, infrastructure as code, Kubernetes, safe deployment and observability
• Experience building engineering guardrails, such as policy as code, supply-chain security and compliance automation, in regulated environments
• Experience with LLM-enabled applications, AI agents or production AI platforms
• Clear and direct communication, with a record of influencing senior leaders without direct authority

Company Overview

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