Principal Software Engineering-CoreAI

As a Principal Software Engineer within Microsoft Foundry, Core AI, you will play a critical role in building and evolving the platform that enables developers and enterprises to design, deploy, and scale intelligent agents and generative AI systems. You will drive technical direction across the full software development lifecycle, owning architectural decisions for complex, large‑scale systems that integrate cutting‑edge AI technologies while meeting the highest standards of quality, reliability, security, and compliance.In this role, you will anticipate and deeply understand customer and developer needs in complex scenarios, translating them into durable platform capabilities and delightful experiences. You will provide technical leadership across teams, guiding design tradeoffs, identifying systemic challenges, and delivering solutions that create long‑term impact while accelerating value to customers. As a senior technical voice, you will mentor engineers, influence without authority, and raise the engineering bar through strong design principles, rigorous code reviews, and a culture of continuous learning.You will collaborate closely with partner teams across Core AI and Azure to ensure seamless integration, scalable architectures, and robust deployment and testing frameworks. You will champion automation, operational excellence, and secure‑by‑design practices, helping define how AI systems and agent platforms are built responsibly and at scale—ultimately shaping how the world interacts with intelligent systems.

AI-Native Development

Drives the improvement of artificial intelligence (AI) tools and practices across the software development lifecycle (SDLC). Proactively takes responsibility for the content of their AI-generated requirements, design documents, code, and other assets, assisting the rest of the team to do the same. Incorporates Responsible AI practices into the SDLC to ensure appropriate controls over AI-generated assets. Intentionally applies SDLC and engineering health measures (e.g., Accelerate, SPACE framework, Engineering System Success Playbook [ESSP]) to guide improvements to processes and practices, especially those involving AI. Experiments with AI tools and practices to improve their own capabilities, and provides recommendations on how to adopt them to the rest of the team.

Coding

Provides technical leadership during code reviews for a solution/product area to assure it meets team standards, contains the correct test coverage, and is appropriate for the product or solution area. Brings expertise to code reviews to help improve code quality, proactively coaching and providing feedback to develop other engineers' skills. Ensures coding standards are followed. Screens for and establishes best practices in reviews and provides feedback on code to drive adherence to best practices. Uses automated source code analysis tools that are incorporated into the build/development process.Leads by example across teams and mentors others to produce extensible, maintainable, well-tested, secure, and performant code used across the company that adheres to design specifications. Leads efforts to continuously improve code performance, testability, maintainability, effectiveness, and cost, while accounting for and incorporating relevant trade-offs. Identifies best practices and coding patterns (e.g., leveraging state-of-the-art generative artificial intelligence [GenAI], approaches to source code organization, naming conventions) and provides deep expertise in the coding and validation strategy. Creates and applies metrics to drive code quality and stability, appropriate coding patterns, and best practices. Leads efforts to identify and anticipate blockers or unknowns during the development process, escalate them, and communicate how they will impact timelines, and then drives the identification and implementation of strategies and/or opportunities to address them.Acts as an expert on using debugging tools, tests, logs, telemetry, and other methods, and proactively leads verification of assumptions through while developing code before issues occur across products and teams in production. Leverages minimal telemetry data, triangulates issues, and resolves with minimal iterations. Leads incident retrospectives to identify root causes of problems, and owns the implementation of repair actions and the identification of mechanisms to prevent incident recurrence. Drives applying least-access principles, using logging, telemetry, and other appropriate mechanisms to investigate issues while retaining privacy and security, and champions those practices across the team.

Design

Establishes best practices and mentors others to