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Open nowPosted 158 days ago

工业ai制造专家

li/referral788 open roles

Where
Beijing, Chinese Mainland
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Your applicationOpen now工业ai制造专家li/referral · Beijing, Chinese Mainland

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on li/referral's own form.

The reply lands in your private mailbox

3×more interviews than doing it yourself with ChatGPT.

The clock on this job

Early applications get read.

11.8% of postings close within 7 days. Measured by our own scanner across the market. li/referral postings stay open a median of 1 days.

Share of postings closed within
  1. 2.1%1 day
  2. 4.4%3 days
  3. 11.8%7 days
  4. 18.5%14 days
  5. 35.5%30 days
  6. 45.1%60 days
This job: posted 158 days ago

li/referral median: 1 days open

The posting

该岗位将面向汽车制造全流程中的关键业务场景，围绕冲压、焊装、涂装、总装、物流及设备运维等环节，构建和落地工业AI能力，推动 AI 从“单点试验”走向“系统赋能”，形成可复制、可规模化推广的产线软实力。 将深度参与制造场景的智能化升级，推动数据、模型、系统与业务闭环融合，打造面向未来汽车工厂的智能决策、智能感知、智能优化与智能运维能力。 岗位职责 面向制造场景规划工业AI解决方案 深入理解汽车制造核心流程及痛点，识别高价值AI应用场景； 围绕质量、效率、成本、交付、安全等目标，制定工业AI应用路线图及落地方案； 推动工业AI从单点工具能力向平台化、体系化能力演进。 推动AI在产线关键业务场景落地 结合视觉、时序数据、日志数据、工艺参数及设备数据，推动AI在以下方向落地： 质量检测与缺陷识别 工艺参数优化与良率提升 设备预测性维护与异常诊断 生产调度优化与节拍提升 能耗优化与资源效率提升 产线知识沉淀与智能问答/智能助手建设 推动模型在真实生产环境中上线、验证、迭代和规模化复制。 建设工业AI工程化与平台化能力 搭建工业AI的数据治理、特征治理、模型训练、部署、监控和迭代机制； 推动制造数据资产化和AI资产沉淀，形成可复用的数据集、算法组件和场景模板； 建立工业AI应用的评估体系，包括业务价值、模型效果、稳定性、可解释性和ROI评估。 跨部门协同与项目牵引 与制造工程、工厂运营、质量、设备、IT/数字化等团队紧密协作，推动复杂项目落地； 作为关键项目负责人或核心专家，牵引跨团队资源，解决实际业务问题； 参与前瞻技术布局，洞察工业AI、智能制造、具身智能等前沿方向，探索面向未来工厂的新型能力。

本科及以上学历，计算机、自动化、机械、电气、工业工程、控制工程、人工智能、数据科学等相关专业； 具备较强的工业场景理解能力，对离散制造、汽车制造或复杂制造系统有深入认知； 具备扎实的AI/机器学习/数据分析能力，能够独立完成问题抽象、建模分析与方案设计； 具备优秀的跨团队协作和项目推动能力，能够将技术方案转化为业务结果。 经验要求 具备工业AI、智能制造、工业互联网、制造数字化相关工作经验； 有整车制造、3C、装备制造、半导体等复杂制造行业经验者优先； 有以下一个或多个方向的深度实践经验者优先： 机器视觉与工业质检 时序数据建模、异常检测、预测性维护 生产优化、调度优化、运筹优化 大模型在工业知识管理、辅助决策、智能助手中的应用 工业数据平台、MLOps、工业AI平台建设 有从0到1推动AI项目落地，并实现规模化复制的成功案例者优先。 技术能力 熟悉机器学习、深度学习、统计分析、优化算法等常用方法； 熟悉 Python 及常见AI/数据开发框架，具备较强的工程实现能力； 熟悉制造业常见数据类型及系统环境，如MES、SCADA、PLC、设备日志、质量数据、工艺数据等； 对模型上线部署、持续迭代、数据闭环、效果监控有完整理解； 对工业场景中的可靠性、实时性、可解释性和可维护性有充分认知。

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