Sr. Cloud Technical Account Manager, ES - NAMER - US-Frontier AI Key job responsibilities Deliver Strategic Technical Engagements - Lead comprehensive technical deep-dives and performance optimization for enterprise AI/ML workloads, including distributed training cluster architecture using AWS Parallel Computing Service (PCS) and AWS ParallelCluster, the latest GPU-accelerated computing (i.e. P6/P6e , G7/G7e instances), AWS Trainium-based training (Trn3 UltraServers), and multi-node NCCL communication tuning over EFA's SRD protocol. Architect and Validate Innovative Solutions - Supporting customers who design and implement production-grade AI/ML training and inference solutions leveraging Slurm-based job scheduling, distributed training frameworks (PyTorch FSDP, DDP, DeepSpeed, Megatron-LM), SageMaker HyperPod for managed GPU clusters with automated health checks and node replacement, high-performance parallel storage (Amazon FSx for Lustre), and container runtimes on Deep Learning AMIs (DLAMIs) against reference architectures and HPC lens to ensure performance, reliability, and cost governance at scale. Architect solutions using P6e UltraServers for multi-trillion parameter frontier models and Trn3 with the AWS Neuron SDK for cost-optimized training and inference. Enable Customer Success - Support customers in implementing business-critical HPC capabilities, including the development of large language model (LLM) (Llama, GPT-class models), physics-informed neural networks (PINNs) and surrogate models, MLOps pipelines, simulation-ML hybrid architectures orchestrated by AWS Step Functions and AWS Batch, distributed data processing, cluster observability, and governance controls for GPU/Trainium-intensive workloads. Enable Business Critical Outcomes - Partner with service teams to enhance model training throughput, optimize NCCL collective communications, improve GPU/Trainium utilization across multi-node UltraClusters, and drive operational efficiency through proactive monitoring, automated failure recovery (HyperPod health checks), and capacity planning (EC2 Capacity Blocks for ML). Contribute to product roadmap PFR, share reference architecture, performance, and benchmarks with broader Technical communities Serve as Trusted Advisor and Advocate - Develop and nurture technical partnerships with enterprise stakeholders, serving as the trusted advisor for AI/ML infrastructure decisions spanning compute, networking (Elastic Fabric Adapter with SRD), storage, orchestration, and the HPC-to-AI convergence journey. A day in the life Your day will be dynamic and impactful, involving deep technical consultations on distributed training architectures, strategic solution design for GPU and Training cluster deployments, and collaborative problem-solving across multi-node ML environments. You'll engage with technical leaders, architect innovative AI/ML implementations - from Slurm-managed training clusters and SageMaker HyperPod to PyTorch FSDP/DeepSpeed training jobs and Neuron SDK compilation workflows - and provide expert guidance that bridges machine learning infrastructure with business objectives. You will partner with Solution Architects, Frontline Engineers, and Service teams to provide customers with AWS AI/ML best practice guidance, diving deep into machine learning infrastructure services (PCS, ParallelCluster, HyperPod, Batch), promoting customers' AI/ML workloads to production, developing regional AI/ML strategies, advising on HPC-to-AI convergence patterns (simulation-surrogate loops, physics-informed neural networks), and training field teams on distributed training patterns, GPU/Trainium cluster operations, and the use cases and benefits of artificial intelligence and machine learning at scale. You establish trust with your customers to understand their business/operational needs and technical challenges and help them achieve the greatest value from AWS. This position will require the ability to travel 10% or more as needed. You will partner with Solution Architects, Frontline Engineers, and Service teams to provide customers with AWS AI/ML best practice guidance, diving deep into machine learning infrastructure services (PCS, ParallelCluster, HyperPod, Batch), promoting customers' AI/ML workloads to production, developing regional AI/ML strategies, advising on HPC-to-AI convergence patterns (simulation-surrogate loops, physics-informed neural networks), and training field teams on distributed training patterns, GPU/Trainium cluster operations, and the use cases and benefits of artificial intelligence and machine learning at scale. You establish trust with your customers to understand their business/operational needs and technical challenges, and help them achieve the greatest value from AWS. This position will require the ability to travel 10% or more as needed. BASIC QUALIFICATIONS - 5+ years of design/implementation/operations/consulting with distributed applications experience - 5+ years of technical engineering experience - Bachelor's degree in computer science, engineering, mathematics or equivalent, or 4+ years of technical work experience - 7+ years of technical engineering experience PREFERRED QUALIFICATIONS - Experience in a 24x7 operational services or support environment - Experience in internal enterprise or external customer-facing environment as a technical lead - 5+ years of experience in AI/ML, distributed computing, or GPU-accelerated infrastructure (e.g., model training, inference systems, HPC for ML) - Familiarity with SageMaker HyperPod for managed distributed training clusters including automated health checks, node replacement, and checkpoint-based recovery - Experience with HPC job schedulers (Slurm, PBS, LSF) for orchestrating multi-node ML training workloads - Experience with high-performance parallel file systems (Amazon FSx for Lustre, GPFS/Spectrum Scale) for ML data pipelines - Familiarity with AWS Parallel Computing Service (PCS), AWS ParallelCluster, AWS Batch, or equivalent managed HPC/ML cluster services - Professional oral and written communication skills, presenting to an audience containing one or more executive team member(s) The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits. USA, CA, San Francisco - 176,600.00 - 239,000.00 USD annually