Senior Software Engineer/Software Engineer II

We are hiring multiple Senior Software Engineers & Software Engineer II's Commercial Engineering & AI (CEAI) is modernizing how Microsoft builds and operates customer, partner, and employee experiences for its commercial business. CEAI brings together agents, engineering tools, data, insights, and direct customer feedback to shorten the path from business need to measurable impact and help Microsoft’s commercial business operate as a frontier organization. The AI Engineering Excellence team defines and scales how CEAI applies AI across the software lifecycle. We establish engineering practices, workflows, and reusable capabilities that teams can adopt broadly while incubating breakthrough approaches that are not yet ready to standardize. AI-native engineering spans the full journey—from understanding customer needs and shaping solutions to building, validating, deploying, operating, and continuously improving them—not merely generating code with AI. Our primary focus is defining, analyzing and improving the agent-driven workflows that drive the software development lifecycle. We prove these capabilities in real engineering environments, measure their impact, turn successful patterns into repeatable practices and scale them across the organization. We will also identify where the organization can benefit from common foundations and platform capabilities, incubate services and tools and scale and operate them. We see a generational opportunity to expand human creativity and productivity through rapid experimentation, disciplined measurement, and continuous learning. As a Senior Software Engineer or Software Engineer II, you will help turn this mission into working software. You will lead the design and implementation of agent-driven SDLC workflows and enabling capabilities, translate ambiguous problems into pragmatic technical plans, and take features from experiment through production use and measurable impact. You will remain hands-on—building prototypes and production systems, evaluating emerging models, frameworks, harnesses and tools, making sound design tradeoffs, and partnering with engineering teams to improve developer productivity, ship velocity, reliability, and the overall engineering experience.

Lead the design and implementation of AI-native engineering workflows and platform capabilities within a defined technical area. Analyze end-to-end software-development workflows and identify where agents, automation, or shared engineering capabilities can remove friction and improve outcomes. Design, prototype, and build production-grade agent-driven workflows, services, and tools spanning areas such as planning, implementation, testing, review, deployment, operations, and learning. Write clear design documents, define interfaces, and create maintainable implementation patterns that teammates and partner teams can safely build on. Evaluate emerging AI development tools, models, and platforms in representative scenarios, considering reliability, security, quality, cost, performance, and appropriate human oversight. Drive scoped 0→1 experiments from hypothesis to working capability, validate them with engineering teams, and use measured results to improve, scale, redirect, or stop the work. Contribute to common foundations and platform capabilities that improve engineering workflows, and help bring resulting services and tools into reliable production use. Own the quality and production readiness of the capabilities you deliver, including testing, observability, security, reliability, performance, cost, incident response, and continuous improvement. Collaborate across teams to resolve dependencies, communicate technical decisions clearly, and influence the design of adjacent systems through sound engineering judgment and demonstrated results. Use AI-native engineering practices in your own work, mentor other engineers, contribute to design and code reviews, and help teams adopt effective