发布于 2026-08-20
英文原文
AI工程师
AI Engineer - LLM Adoption Department (LLMAD)
楽天グループ
| 职种 | 工程 | 雇用形态 | — |
|---|---|---|---|
| 工作地点 | 东京 | 薪资 | 面议 |
职位概要
乐天集团AI与数据部门(AIDD)的LLMAD团队招聘AI工程师,负责推动乐天自研及开源大语言模型在集团内部业务中的实际应用。该职位需嵌入业务单元工程团队,日常参与智能体工作流与工具调用集成、提示词迭代、模型评估及生产环境上线。主要职责包括:在业务单元代码库中编写生产代码,实现函数调用、多步流程、检索、护栏与回退处理;将功能从原型推进至生产并支持调试、优化延迟与成本;针对业务场景对自研及开源模型进行任务级评估,维护评估集与测试框架;设计并迭代提示词、输出模式与工具定义,基于评估而非主观感觉进行版本管理与度量;支持从第三方API迁移至自研模型,并向高级工程师及模型平台团队反馈差距;将有效实践整理为可复用配方、示例与模板,帮助业务单元工程师采用内部工具与API。必备资格:3至6年专业软件工程经验,含LLM应用开发实操;具备智能体工程实践经验,熟悉工具/函数调用或编排框架并能调试异常;掌握提示词工程知识,包括结构化输出、基于测试用例迭代及版本化管理意识;至少精通Python、Java、Go等一门现代语言,熟悉API、后端服务与测试;对模型行为有好奇心,能深入分析错误原因;能嵌入他人团队工作,直接与业务方沟通并适应一定模糊性。优先条件:日语能力(重要加分项,业务团队日常使用日语);具备检索增强生成经验;熟悉评估框架或LLM可观测性工具;了解自托管推理(如vLLM)或云模型部署。
职位描述
Job Description: Business Overview Rakuten Group, Inc. is a global leader in internet services that empower individuals, communities, businesses, and society. Founded in Tokyo in 1997 as an online marketplace, Rakuten has expanded to offer services in e-commerce, fintech, digital content, and communications to approximately 1.7 billion members around the world. The Rakuten Group has nearly 32,000 employees and operations in 30 countries and regions. For more information visit https://global.rakuten.com/corp/ Department Overview The AI & Data Division (AIDD) creates powerful, customer-focused search, recommendation, data science, advertising, marketing, price, and inventory optimization solutions to a variety of businesses in the Rakuten group. Our goal is to drive innovation by developing new products and capabilities that deliver significant impact over longer timeframes using AI. The LLMAD team drives adoption of Rakuten AI and open-source LLMs hosted within our own environment. We're hiring AI Engineers who can be embedded directly inside business units to drive real adoption of in-house large language models in place of third-party alternatives. Position: Position Details As an AI Engineer you will be embedded inside a business unit's engineering team and build with them day to day: implementing agent workflows and integrations, iterating on prompts, running evaluations, and getting features live in production on our in-house models. You will work on an engagement alongside a senior engineer who sets the overall agent architecture and model direction, and you will take growing ownership of the delivery as you build depth. This is hands-on applied LLM work, close to the product. ã»Embed inside a business unit's engineering team and implement agent and tool-use workflows for their use case - function calling, multi-step flows, retrieval, guardrails, and fallback handling - writing production code in their codebase. ã»Get features from prototype into production and support them there: debugging failures, tightening latency and cost, and fixing what breaks. ã»Run task-level evaluations of in-house and open-source models for your use case, and extend and maintain the eval sets and harnesses together with the business unit. ã»Design and iterate prompts, output schemas, and tool definitions - versioned and measured against evals rather than tuned by feel - and help narrow quality problems down to the prompt, the retrieval, the model, or the data. ã»Support migrations from third-party APIs to our in-house models, and surface the gaps you find to the senior engineer on the engagement and to our model and platform teams. ã»Document what worked as reusable recipes, examples, and starter templates for the team's shared library, and help business unit engineers adopt our tools and APIs. Mandatory Qualifications: ã»3 to 6 years of professional software engineering experience, including hands-on work building LLM applications. ã»Practical agentic engineering experience: you have built agents using tool and function calling or an orchestration framework and have debugged them when they misbehave. ã»Working knowledge of prompt engineering: structured outputs, iteration against test cases, and the understanding that prompts need versioning and measurement. ã»Strong fundamentals in at least one modern language (e.g., Python, Java, Go), and comfort with APIs, backend services, and testing. ã»Curiosity about how models behave - you want to understand why an output was wrong, not just retry it. ã»Comfortable working embedded in someone else's engineering team, communicating directly with business stakeholders, and operating with a degree of ambiguity. Desired Qualifications: ã»Japanese language ability - a significant plus. Our business unit teams work in Japanese day to day, and being able to work in the language directly makes the embedded model far more effective. ã»Retrieval-augmented generation experience. ã»Familiarity with evaluation frameworks or LLM observability tooling. ã»Exposure to self-hosted inference (e.g., vLLM) or cloud model deployment. #engineer #applicationsengineer #aianddatadiv In Japanese, Rakuten stands for âoptimism.â It means we believe in the future. Itâs an understanding that, with the right mind-set, we can make the future better by what we do today. So we challenge ourselves to evolve, innovate and experiment, to create a better, brighter future for everyone. Today, our 70+ businesses span e-commerce, digital content, communications and fintech, bringing the joy of discovery to almost 1.3 billion members across the world. If you have any trouble logging in, please contact us here Rakuten Group, Inc.: rakuten-recruiting-info@mail.rakuten.com *Please read the Application Requirements(EN) / åéè¦é (JP) before applying. Our Diversity & Inclusion Policy and Application Documents Rakutenâs corporate mission is to âcontribute to society by creating value through innovation and entrepreneurship.â We foster a culture that provides equal opportunities to those who share this founding philosophy and take on the challenge to transform society, regardless of age, gender, nationality, or any other status. Diversity is one of Rakuten's core strategies and a driving force for innovation. Because of this, you are not required to submit any of the following information in order to apply for our job positions. - Gender - Age - Photo - Nationality* - Information not related to business, such as ideological beliefs, family structure, etc. * For legal compliance, we may ask you about your work eligibility. See the details