AI System Performance Architect

About This Role

• About the Role We are looking for a talented and motivated AI System Performance Architect.
• The role owns the end-to-end understanding of AI training and inference systems, sitting where serving strategy, parallelism, collective communication, and interconnect behavior come together.
• Through analytical modelling and multi-GPU simulation, the candidate translates SLA and deployment targets into concrete architectural guidance for Intel's future AI platforms.
• Strong analytical skills and the ability to present performance findings and configuration recommendations to engineering and leadership teams are essential.
• Key Responsibilities • Own end-to-end understanding of the AI training/inference system and build analytical models and multi-GPU simulation - including MPI/collective communication (allgather, allreduce, all-to-all) and interconnect contention - to project performance, expose architectural bottlenecks, and recommend optimal Intel system configurations. • Model inferencing strategies - static batching, continuous batching, disaggregated serving, and chunked prefill - across frameworks such as vLLM and SGLang, covering agentic inferencing, prefix caching, and multi-turn flows. • Cover data, pipeline, tensor, and expert parallelism and their communication patterns. • Drive studies against SLA targets - batch size, TTFT, TPOT, ISL/OSL, and TPS. • Analyse regression run traces and find patterns in utilization counters across SLA operating points to identify architectural bottlenecks. • Perform what-if analysis by sweeping core count, memory/cache capacity and bandwidth, fabric bandwidth, GPU interconnect, and deployment options; deliver optimization and optimal architecture recommendations. • Scout whitepapers, blogs, and conferences; keep self and team updated on AI innovations applicable to Intel's end-to-end system.