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
The AI Solution Architect joins AWS’s AI specialist Solutions Architect team, bridging AWS and customers adopting generative AI and machine learning. It leads GenAI/ML and Agentic AI initiatives, designing resilient cloud architectures for customer solutions.
It turns technology into customer solutions and defines cloud-native patterns for custom models and agentic applications. Core work includes large-language and multimodal foundation-model integration, fine-tuning, deployment, distributed inference, RAG, vector databases, agentic workflows, prompt/context engineering and MLOps. It also creates reusable code, reference architectures and technical workshops.
Applicants need five or more years designing and implementing production AI systems, clear communication with technical and business stakeholders, and the ability to explain advanced technology plainly. Native-level Japanese reading, writing and conversation is required, as are English reading and writing and willingness to participate in English discussions; AWS or cloud expertise is not required at application.
This is an AI-generated summary of the employer's original posting — details can be incomplete, out of date or simply wrong.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.
Design production-grade AWS data architectures for enterprise customers, spanning analytics, warehouses, data lakes, pipelines, streaming, search, governance, security, cost, and operations.
Leads end-to-end customer deployments, builds and manages a field engineering organization, develops delivery tools and playbooks, and reviews production code across frontend and backend systems.
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.