产品解决方案专家（政企 AI 方向）
上海、北京、深圳
社招
全职
技术 - 基础架构
职位 ID：A182031
职位描述
职位描述 你将负责政企 AI 产品或解决方案从需求发现到交付落地的完整链路。面对委办局、科研院所、央国企、运营商、工业企业等客户，把“想用 AI”进一步拆成清晰的业务流程、可验证的产品能力和可交付的技术方案。你可以从产品规划出发，也可以从售前方案切入。你会与算法、研发、架构、数据和交付团队一起工作，将 MiniCPM 系列模型、知识库、RAG、多智能体、数字员工及私有化一体机能力，落到数字员工、公文办公、情报研判、质量质检、装备管理等真实场景中。我们期待你既能看懂客户的业务，也能把方案推进到上线和持续迭代。 岗位职责 1. 深入理解政企客户的业务目标、组织流程、数据边界和使用环境，完成用户访谈、场景分析、需求拆解和价值判断。 负责 AI 产品或行业解决方案的规划、设计和迭代，输出产品路线图、PRD、业务流程、交互原型、方案说明及验收标准。 2. 围绕智能问数、知识库问答、公文与报告生成、文档解析、情报研判、数字员工、多智能体等方向，定义可落地的产品能力和业务闭环。 3. 与算法、研发、架构、数据和交付团队协同，将客户需求转化为可开发、可测试、可交付的任务，推动 POC、试点、上线和验收。 4. 参与私有化部署、一体机和端云边协同方案设计，明确模型、知识库、工具、权限、日志、硬件和网络环境之间的关系。 5. 建立产品效果指标和反馈机制，持续跟踪准确率、召回质量、任务完成率和使用频次，并沉淀行业模板、交付方法和案例材料。
职位要求
投递
ModelBest established in August 2022, is an Artificial Intelligence technology company headquartered in Beijing, China. The company is deeply rooted in the field of general AI, with a focus on the innovation and application transformation of large-scale models. ModelBest boasts a prestigious founding R&D team from the Tsinghua in the AI field. Leveraging numerous cutting-edge technologies in natural language processing, the company is currently constructing a large-scale pre-trained model library and corresponding tools, aiming to standardize the technology and applications of large models.
Based on the capabilities of the CPM series of large models, ModelBest has successfully facilitated the intelligent upgrade and efficiency enhancement of various industries. Moreover, the company has released a multi-modal dialog assistant with hundreds of billions of parameters, “Luca", to the public. Adhering to the core philosophy of "intelligence encompassing all", we look forward to exploring and expanding unknown possibilities with global partners, aiming to ensure AI technology serves humanity for a better life in a safe and inclusive manner, laying a solid foundation for the advent of the AGI world.
OpenBMB (Open Lab for Big Model Base，https://github.com/OpenBMB), founded by ModelBest Inc & TsinghuaNLP, aims to build foundation models and systems towards AGI.
