Join Microsoft's Engineering Operations (EngOps) team as a Principal Applied AI Engineer, shaping the future of cloud customer support through AI-first systems. This role focuses on transforming customer interactions from reactive workflows to intelligent, AI-driven solutions that resolve issues at Azure scale. You'll be a hands-on technical architect and developer, defining and implementing cutting-edge AI agent architectures. This is a critical individual contributor position with significant architectural influence and direct accountability for production outcomes.

Our team is dedicated to enhancing cloud quality, security, and reliability by deeply engaging with customers and internal teams. We foster a culture of continuous growth and empowerment. You will have the opportunity to build foundational components, make pivotal design decisions, and lead complex technical initiatives, ultimately impacting how thousands of customers receive support for their critical Azure services.

Architect and develop robust, production-ready agentic AI systems, encompassing LLM orchestration, multi-agent collaboration, retrieval-augmented generation (RAG), and integrated tool functionalities. Lead technical delivery from concept to operation, translating intricate customer support challenges into scalable solutions. Establish and uphold stringent engineering standards for AI services, including architecture patterns, code quality, evaluation methodologies, and deployment strategies.

Drive AI development through advanced evaluation frameworks, ensuring AI services meet high standards for correctness, performance, and safety. Design and implement secure AI solutions embedding responsible AI principles and robust defenses against vulnerabilities. Make critical technical decisions by evaluating diverse technologies and balancing system quality with operational efficiency. Influence product roadmaps and drive measurable customer and business impact through data-informed improvements.

A Bachelor's degree in Computer Science, Engineering, or a related technical field is required, along with a minimum of 12 years in professional software engineering. Demonstrated expertise in C# and/or Python, proficiency in building production-quality services and distributed systems, and experience as a technical architect or lead on complex initiatives are essential. A strong background in applied AI systems, including LLM applications, RAG, or ML services, coupled with experience in cloud platforms like Azure and cloud-native practices, is expected.

Proven ability to own the complete engineering lifecycle, from problem definition to production operations, is crucial. Candidates must possess deep knowledge of software architecture, distributed systems, and service reliability. The ability to influence technical direction and align teams without direct management authority is a key requirement. A Master's degree in a relevant technical field and experience with multi-agent systems or AI evaluation frameworks are highly preferred.