Principal Software Engineer

Overview

You will deliver production-ready AI-enabled solutions by embedding within customer teams. In this role you build reliable systems and scalable services, guiding end-to-end architecture from design to deployment. You work with cross-functional crews to translate business needs into technical outcomes and drive customer success at scale. You apply cutting-edge AI and secure engineering practices to impact enterprise outcomes and contribute to Microsoft’s mission of empowering every person and organization.

Responsibilities

• Prioritize security by designing and shipping solutions that meet enterprise standards
• Collaborate with stakeholders to translate business needs into architectural approaches and success metrics
• Own end-to-end system design for cloud and AI workloads with tradeoffs across reliability, performance, and cost
• Deliver rapidly using CI/CD, automated testing, and observability while managing operational risk
• Work with customer engineering teams to ensure adoption and measurable outcomes at scale
• Create reusable assets, accelerators, and reference architectures for broader use
• Navigate ambiguity, bring clarity, and maintain momentum across complex engagements
• Provide technical leadership, mentor engineers, and collaborate across product, data, and security teams
• Coordinate multiple workstreams to improve reliability and production-grade operations
• Model inclusive leadership and represent the company professionally to external stakeholders

Key requirements

• Bachelors Degree in Computer Science or related field and coding experience in C, C++, C#, Java, JavaScript, or Python
• Experience partnering with customers or internal stakeholders to deliver end-to-end solutions
• Experience designing, deploying, and operating AI or LLM-based solutions, including prompt engineering and model tuning
• Familiarity with deploying and operating AI systems in production environments
• Experience using cloud AI platforms (Azure ML, OpenAI, or similar) and evaluating data quality and performance monitoring
• Willingness to travel up to 25%

• leadership
• collaboration
• communication
• AI/LLM solution design and deployment
• prompt engineering
• retrieval-augmented approaches

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