Principal Technical Account Manager, AWS Enterprise Support, NAMER-Sp

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 our team and become a strategic partner in delivering Amazon AI/ML solutions that empower global enterprises to innovate, optimize, and achieve unprecedented operational excellence.

Amazon Web Services (AWS) is seeking an experienced Principal AI/ML HPC Specialist to join our Technical Account Manager (TAM) team.

You'll be at the forefront of solving complex AI HPC implementation challenges, guiding NAMER Research labs to enterprise 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.

AWS Support includes experts from across AWS who help our customers design, build, operate, and secure their cloud environments. Customers innovate with AWS Professional Services, upskill with AWS Training and Certification, optimize with AWS Support and Managed Services, and meet objectives with AWS Security Assurance Services. Our expertise and emerging technologies include AWS Partners, AWS Sovereign Cloud, AWS International Product, and AI/ML-native solutions. You'll join a diverse team of technical experts in dozens of countries who help customers achieve more with the AWS cloud.

• 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 - 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, share reference architecture performance and benchmarks with broader TAM and 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 Trainium 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 PCS 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 TAMs, SAs, 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.

• About the team
• Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn't followed a traditional path, or includes alternative experiences, don't let it stop you from applying.

Amazon Web Services (AWS) is the world's most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that's why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.

We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there's nothing we can't achieve in the cloud.

Here at AWS, it's in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity and AmazeCon conferences, inspire us to never stop embracing our uniqueness.

We're continuously raising our performance bar as we strive to become Earth's Best Employer. That's why you'll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.

Basic qualifications

• - 8+ years of working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage experience
• - 3+ years of hands-on experience designing, implementing, or consulting on large-scale ML training or inference architectures in a customer-facing role
• - 10+ years of IT development or implementation/consulting in the software, cloud computing, or AI/ML industries
• - Demonstrated ability to serve as a trusted technical advisor to enterprise customers

Preferred qualifications

- Experience with deep learning libraries such as PyTorch, TensorFlow, MxNet Research publications in computer vision, deep learning or machine learning at peer-reviewed workshop