ML Operations Engineer (AI/LLM) - Mercari

本ポジションは日本語JDの用意がありません。 ML Operations Engineer (AI/LLM) - Mercari Employment Status:　Full-time Work Hours: Full Flextime (no core time) Office: Roppongi For more details, see the Overview of Our Positions section on our Careers site. 　 About Mercari Circulate all forms of value to unleash the potential in all people "What can I do to help society thrive with the finite resources we have?" The Mercari marketplace app was born in 2013 out of this thought by our founder Shintaro Yamada as he traveled the world. We believe that by circulating all forms of value, not just physical things and money, we can create opportunities for anyone to realize their dreams and contribute to society and the people around them. Mercari aims to use technology to connect people all over the world and create a world where anyone can unleash their potential. For more information about Mercari Group’s mission, see Mercari’s Culture Doc 　 Organization/Team Mission Mercari Engineering Principles Mercari Engineering Principles are a shared understanding that serves as the foundation of engineering beliefs and behavior at Mercari. The Engineering Principles are designed to complement the organizational identity (Mercari’s mission, values, and culture) from an engineering viewpoint. These principles ultimately help us achieve Mercari’s mission by defining the ideal state we seek to realize in the long term. Passion For The Product Grow Together Solve Through Mechanisms Collaborate Openly For more details, please see the following link: Engineering Culture The AI / LLM Team’s mission is focused on three core pillars, “product”, "enablement" and "research", delivering new AI-driven features and user experiences to maximize product-facing impact for Mercari's business. We do this both through independent initiatives owned by our team, as well as by horizontally collaborating with product, engineering, and research teams across the entire organization. As an MLOps engineer on the AI/LLM team, you will own how our machine learning and LLM models reach production and stay healthy there in our cloud-native environment. Your focus is the production serving, deployment, and operations that turn models into reliable, cost-efficient services, seamlessly integrating with our machine learning operations to serve tens of millions of users. 　 Work Responsibilities Data and Model Orchestration: Own the end-to-end orchestration of model inference, including integrating with DataServ for retrieval (BigQuery, BigTable, Valkey) and managing the Model Inference Gateway and Console. Model Serving and Deployment: Own production model serving on the cloud-native NVIDIA and TPU stacks (Triton Inference Server, TensorRT-LLM, JAX/TPU Gateways). Manage model repositories, dynamic batching, and concurrent model execution. Build CI/CD, rollout, and rollback paths for safe, scalable model deployment, and automate provisioning and lifecycle management (Terraform, Kubernetes)…