AI Infrastructure System Performance Architect

Position: AI Infrastructure System Performance Architect (IRT815ST RM 4407)

Position Summary
Analyze system-level performance, scalability, and bottlenecks of the company s chiplet-based AI infrastructure using virtual platforms and performance models.

Education : B.E. / B.Tech / M.E. / M.Tech in Electronics, Electrical, Computer Engineering, or related discipline.

Key Responsibilities

• Memory / Data-Movement Modelling: Analyze memory bandwidth and latency requirements, model XPU-to-memory and chiplet-to-memory traffic, and identify data-movement bottlenecks.
• Multi-Chiplet / Multi-XPU Scalability: Analyze scaling from single-XPU to multi-XPU systems, evaluate scale-up/scale-out architectures, and study bandwidth, latency, congestion, and resource utilization.
• AI Infrastructure System Modelling: Develop system-level performance models covering compute, interconnect, memory, and I/O interactions, and develop representative AI/HPC traffic patterns.
• System Performance Architecture Exploration: Perform architecture trade-offs and analyze latency, bandwidth, throughput, utilization, queue depth, congestion, and bottlenecks; generate performance reports and architecture recommendations.

Required Technical Skills

• 8-15+ years of system/performance architecture experience.
• AI/HPC infrastructure and performance modelling.
• SystemC/TLM.
• C++ / Python.
• PCIe / CXL / UCIe.
• HBM/DDR.
• System-level architecture.

Preferred Skills

• XPU/GPU/NPU architecture.
• AI workload characterization.
• UALink and networking.
• Cluster/rack-scale architecture.
• Power-performance analysis.

Project Experience : Demonstrated system-level performance analysis (latency, bandwidth, throughput, utilization, congestion) on a chiplet-based or multi-XPU AI system.

Soft Skills

• Strong analytical rigor in translating raw performance data into architecture recommendations.
• Ability to collaborate across connectivity, memory, and power architecture teams to build a coherent
  system view.

Good to Have

• Experience characterizing real AI/HPC workloads for use in performance models.
• Exposure to cluster or rack-scale system architecture.

Expected Deliverables

• System-level performance models covering compute, interconnect, memory, and I/O.
• Architecture trade-off studies and performance reports with recommendations.
• Representative AI/HPC traffic pattern definitions for use across the team s models.

Growth Path

• Progress into Principal Performance Architect, System Architecture Lead, or Practice Lead (Performance Engineering) roles.