Design and implement highly scalable, reliable, and performant distributed systems that power Azure AI traffic management and scheduling. Drive architecture and technical direction for routing, capacity allocation, and workload optimization services. Develop algorithms and control-plane systems that intelligently distribute AI workloads across global infrastructure while meeting latency, availability, and cost objectives. Collaborate with partner teams to onboard new models, regions, and customer offerings while maintaining operational excellence. Build observability and operational tooling to monitor service health, capacity utilization, traffic patterns, and customer impact in near real-time. Lead investigations into complex production issues and drive systematic improvements to reliability, resiliency, and platform automation. Identify and execute opportunities to improve fleet efficiency, reduce operational overhead, and optimize utilization of premium AI infrastructure. Mentor engineers, review designs and code, and raise engineering standards across the organization. Drive engineering excellence through testing, automation, documentation, and operational best practices. Influence long-term platform strategy and contribute to roadmap planning for next-generation AI infrastructure. Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Master'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 Bachelor's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. Experience with telemetry, metrics pipelines, or resource scheduling systems. Familiarity with cloud platforms (Azure, AWS, GCP) and container orchestration (Kubernetes, Service Fabric). Experience operating large-scale services with stringent reliability and availability requirements. Experience with distributed storage systems, telemetry platforms, and real-time data processing. Familiarity with AI/ML infrastructure, GPU fleets, inference serving systems, or large-scale model deployment platforms. Experience leading cross-team initiatives and driving projects from design through production deployment and operation.