Work directly with business stakeholders to translate between data and business needs, analyze results, and communicate findings to senior leadership in the Tokyo-based Japan Central Ops role.
About this role
This role is part of AWS’s Worldwide Specialist Organization and its GenAI Training and Inference team. It helps enterprise, startup, partner, and public-sector customers adopt and scale large language model training and inference workloads on AWS, while shaping go-to-market plans for the GenAI business.
It develops customer solutions, demos, and proofs of concept; benchmarks and optimizes models on accelerated GPU, TPU, and AI-ASIC clusters; and supports large-language-model serving through Kubernetes. Named technologies include Amazon EC2, Lustre, PyTorch, JAX, Docker, MLOps workflows, NCCL, and MPI. The role also connects engineering, product, research, account, sales, and field teams.
Applicants need 8+ years in relevant technology domains and 3+ years designing, implementing, or consulting on applications and infrastructure. Required experience covers AI products at scale, ML and LLM fundamentals, model optimization on accelerated clusters, Kubernetes-based inference, deep-learning infrastructure, MLOps workflows, and field-team work driving ML adoption.
This is an AI-generated summary of the employer's original posting — details can be incomplete, out of date or simply wrong. Always confirm everything on the official posting before applying.
Similar jobs
Applied GenAI engineering role for Amazon Logistics requiring AWS data services and big-data technologies, including Redshift, S3, Glue, EMR, Kinesis, Hadoop, Hive, and Spark.
Develop machine-learning models, run experiments, and analyze large-scale data to improve product discovery, search relevance, personalization, and GenAI/LLM-powered customer experiences.
Leads technical collaborations and research on hybrid quantum-classical applications for AI, high-performance computing, simulations, and digital twins.
Lead platform engineering for AI-native software development and operations, building automation, quality, monitoring, and secure systems for sensitive medical data.
Lead technical strategy across Smaregi products, design shared foundations, establish quality standards and resolve engineering bottlenecks with teams.