Principal Software Engineer Architect

In this role you will lead the design and adoption of AI-native supply chain architectures to enable hyperscale operations and automation. You will work closely with product managers, engineers, and business stakeholders to evolve the C+AI ecosystem and improve AI model quality through optimized data pipelines. You will partner across teams to implement AI-powered supply chain solutions and design measurable operational outcomes. You will provide technical leadership for enterprise software solutions that scale AI-driven supply chain operations, shaping Microsoft's Cloud and Devices Supply Chains.

• Lead design and adoption of AI-native supply chain architectures in partnership with product managers, engineers, and business stakeholders
• Define and evolve the C+AI supply chain ecosystem to support hyperscale operations, automation, and continuous improvement
• Partner across engineering, product, and business teams to align priorities and implement supply chain solutions
• Improve quality and reliability of AI models by optimizing data pipelines, orchestration systems, and operational workflows
• Define, develop, and monitor performance metrics for AI-driven supply chain architectures
• Design and build intelligent decision-making platforms that deliver measurable operational and business outcomes
• Develop and scale context engineering approaches that enhance AI-powered applications for supply chain management
• Provide technical leadership in the design and scaling of enterprise software solutions for AI-powered supply chain operations

• 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 OR equivalent experience
• 5+ years of experience developing enterprise scale cloud and AI based architectures.

• cross-functional collaboration
• data-driven decision making
• influencing teams
• AI-native supply chain architectures
• data pipelines and orchestration
• performance metrics for AI architectures