Senior Solutions Architect, AI Factory Deployment

What you'll do

• Set up, adjust, and verify AI factory environments across multi-GPU and multi-node Linux clusters.
• Validate configurations for NCCL, collectives, and distributed training frameworks.
• Run, orchestrate, and analyze key AI/LLM benchmarks.
• Troubleshoot and resolve issues when training jobs or benchmarks fail, hang, or underperform.
• Build observability tools including metrics, logs, traces, and dashboards to monitor system health.
• Develop Python and Shell scripts to automate benchmarks, result retrieval, and regression checks.
• Analyze communication patterns and NCCL usage to improve throughput, latency, and scaling efficiency.
• Validate hardware and software configurations to prepare AI factories for customer use.

What we're looking for

• Bachelor's degree or equivalent experience in Computer Science, Mathematics, Engineering, Physics, or a related field.
• 5+ years of experience managing Linux-based systems in HPC, distributed systems, or AI/ML environments.
• Hands-on experience running AI/ML workloads on multi-GPU and/or multi-node clusters, including exposure to NCCL.
• Practical knowledge of collective communication patterns like AllReduce and AllToAll for ML/LLM training.
• Skilled in Python and Shell/Bash for scripting, automation, and tooling.
• Strong communication skills and the ability to work effectively with cross-functional teams.
• Experience benchmarking distributed systems, including crafting, running, and interpreting performance benchmarks (preferred).
• Background in HPC performance engineering, SRE, or systems performance analysis for GPU-accelerated environments (preferred).