Senior AI Network Systems Engineer

AI Network Architecture & System Integration Define and develop networking requirements for large-scale AI training and inference clusters. Collaborate with silicon, system software, firmware, hardware, and Azure infrastructure teams to deliver scalable networking solutions from concept through datacenter deployment. Participate in architecture reviews and influence next-generation AI networking roadmaps. Define network concepts of operation, serviceability requirements, telemetry requirements, and operational models for AI infrastructure. Analyze transport-layer behavior and performance characteristics across large-scale distributed AI workloads. Evaluate network protocol implementations and debug issues impacting latency, throughput, scalability, and reliability. Design, validate, and optimize RDMA-based networking solutions for AI clusters. Analyze RDMA performance, congestion behavior, packet loss, retransmissions, and collective communication efficiency. Work closely with networking vendors and software teams to optimize AI fabric performance and workload scalability. Develop validation methodologies for AI traffic patterns and collective communication workloads. Performance Characterization & Validation Develop and execute networking validation strategies covering functionality, performance, scale, interoperability, resiliency, and reliability. Evaluate latency, bandwidth utilization, congestion events, flow distribution, and workload communication patterns. Perform packet-level analysis and protocol debugging using telemetry, packet captures, performance counters, and diagnostic tools. Investigate network switch, NIC, RDMA, routing, congestion control, and protocol-related issues. Build and improve network observability, diagnostics, telemetry, and monitoring solutions. Develop tools and automation for network validation, performance analysis, and failure detection. Improve engineering productivity through automated testing, qualification, and network health assessment frameworks. Master's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 3+ years technical engineering experience OR Bachelor's Degree in Electrical Engineering, Computer Engineering, Mechanical Engineering, or related field AND 5+ years technical engineering experience OR equivalent experience. 5+ years of experience developing or validating networking for accelerator based systems. 5+ years of experience designing, integrating, validating, or troubleshooting Ethernet-based networking infrastructure, including Network switches. 5+ years of experience supporting AI, HPC, cloud, or large-scale data center infrastructure deployments. These requirements include but are not limited to the following specialized security screenings: Experience with RDMA technologies, AI fabrics, and distributed training environments. Understanding of RoCE, congestion control, ECN, PFC, DCQCN, and related AI networking technologies. Experience with AI/ML workload communication patterns and collective operations. Experience with SONiC, Linux networking, networking telemetry, and network operating systems. Experience with network switches, SmartNICs, DPUs, NIC offloads, and large-scale cloud infrastructure. Familiarity with AI networking technologies including Ultra Ethernet and hyperscale AI cluster architectures. Experience developing network stress tools, validation frameworks, performance benchmarks, or observability solutions. Knowledge of packet analysis tools, telemetry infrastructure, and network automation frameworks. Exposure to high-speed networking environments (200G/400G/800G Ethernet).