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GenAI Infra Specialist SA, AWS WWSO Startup

Amazon· Solutions Architect
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Hybrid (New York, NY), Hybrid (Herndon, VA), Hybrid (Seattle, WA)$144.5k - $195.4kfull-timePosted yesterday

About the role

Specialist Solutions Architect role focusing on GenAI infrastructure for AWS Startup customers. Responsible for AI model training and inference optimization, hardware/training infrastructure guidance, and collaboration across AWS teams to accelerate adoption of AWS compute, networking, and ML platform services for frontier AI workloads.

Responsibilities

  • Work directly with startup customers on GenAI model training and inference at scale on AWS
  • Advise on AI infrastructure requirements and trade-offs (GPU/Trainium, cluster topology, storage, networking, cost optimization)
  • Provide guidance on inference optimization architectures (self-managed on EKS, SageMaker endpoints, SageMaker Hyperpod Serving), batching, quantization, model parallelism, latency/throughput tradeoffs
  • Guide training optimization and distributed training strategies (frameworks like PyTorch, JAX, NeMo; SageMaker HyperPod; Slurm/PCS; checkpointing; data pipelines)
  • Profile GPUs/accelerators to identify bottlenecks and optimize performance
  • Explain model optimization techniques (LoRA, QLoRA, RLHF/DPO, knowledge distillation, efficient serving techniques)
  • Help define growth strategy with GTM on assets, market sizing, opportunity pipeline, and technical content for field teams
  • Develop demos, proofs-of-concept, reference architectures, and benchmarks to demonstrate AWS value for GenAI workloads
  • Collaborate with product teams across EC2, Trainium/Inferentia, SageMaker, EKS, PCS, EC2 to shape product vision
  • Work with customers, partners, ISVs, framework communities, and model providers to drive implementations and accelerate innovation

Qualifications

  • 2+ years of design, implementation, or consulting in applications and infrastructures experience
  • 5+ years of experience in technology domain areas (e.g., software development, cloud computing, systems engineering, infrastructure, security, networking, data & analytics)
  • Experience conveying complex technical concepts to both technical and business audiences
  • 5+ years of experience in technology domain areas (e.g., systems engineering, cloud infrastructure, HPC, ML/AI, distributed computing)
  • 2+ years of experience designing, implementing, or consulting on large-scale AI/ML infrastructure with hands-on experience on GPU-based computing, ML training infrastructure, and inference serving systems

Skills

Required:GPU-based AI infrastructureDistributed training and inference optimizationFrameworks and tools for ML

Benefits

  • Health insurance (medical, dental, vision, prescription)
  • 401(k) matching
  • Paid time off and parental leave
  • Employee stock units (RSUs)
  • Flexible spending accounts
  • EAP and mental health support
Last updated Oct 25, 2026. Interested? Apply on the company site.

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