Sr. Cloud Technical Account Manager, ES - NAMER - US-Frontier AI

Amazon Web Services, Inc.; San Francisco (CA); On-site; USD 160,000 - 220,000; Full time

Job summary

AWS Frontier AI Solutions is seeking a Sr. TAM to join the Frontier AI Technical Account Management team in San Francisco.

You will guide Frontier research customers through ambitious ML transformations, helping design, build, operate, and secure cloud AI/ML environments. Responsibilities include leading technical deep-dives, optimizing AI/ML workloads, and coordinating with service teams to ensure performance, reliability and cost governance at scale.

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

Job description

AWS Applied AI Solutions (AAIS) is building toward a future where every business innovates with Amazon AI teammates. To get there, we build AI solutions that improve human capabilities and transform entire business functions. We create end-to-end products that surprise and delight out-of-the-box, making complex things easy and hard things possible, with no cloud experience required. We start with customers who embrace the future and build bridges to meet the rest where they are. We pursue ambitious opportunities with conviction, and we are looking for builders who share that mindset.

Are you ready to transform how businesses leverage artificial intelligence and machine learning at scale? Join the Amazon Web Services (AWS) Support team and become a strategic partner in delivering Amazon AI/ML solutions that empower our Frontier AI customers to innovate optimize and achieve unprecedented operational excellence.

Amazon Web Services (AWS) is seeking an experienced Sr. TAM with expertise in AI/ML HPC and/or other technologies to join our Frontier AI Technical Account Management (TAM) team.

You’ll be at the forefront of solving complex AI/ML model training and inference implementation challenges guiding Frontier research customers through their most ambitious machine learning transformation journeys. By combining deep technical expertise with collaborative problem-solving you’ll help organizations unlock the full potential of artificial intelligence and machine learning technologies - from distributed model training on GPU clusters to production-grade inference at scale.

The TAM role is not directly hands on keyboard within the customer’s environment for troubleshooting customer support issues rather you will work with appropriate engineers and service teams to see issues through to resolution. You will help our customers design build operate and secure their cloud environments.

More importantly you will work proactively to help craft and execute strategies to drive our customers’ adoption and use of AWS services including EC2 S3 DDB RDS and many more.

Your technical acumen and customer-facing skills will enable you to effectively represent AWS within a customer environment and drive discussions with senior leadership regarding incidents trade-offs support and risk management. You will provide advocacy and strategic technical guidance to help plan and build solutions using best practices and proactively keep your customers' AWS environments operationally healthy and resilient.

The close relationships developed with your customers will allow you to understand their business/operational needs and technical challenges to help them achieve the greatest value from AWS. This position will require the ability to travel 10% or more as needed.

The TAM is the centerpiece of value to our Enterprise Support customers. If you wish to be at the forefront of innovation come join us!

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