【創世紀】AI架構師

1.系統設計開發人員 

2.系統開發可行性評估、系統DFX檢查、系統排線繞線定義、定制系統需求規格書 

3.精通英文、溝通協調能力

1. 熟悉自然語言處理 (NLP)，讓機器人可進行語音辨識、情感識別 

2. 熟悉機器學習、強化學習等AI訓練，給予機器人決策、行為生成能力

1. 建構 x86 / ARM 平台之 Linux RTOS 執行環境 

2. 設計與整合 EtherCAT / Modbus 等通訊協定與驅動模組 

3. 串接伺服器、感測器、相機等設備通訊控制 

4. 負責控制模組與 AI 模型的整合部署與環境建置

1.組織日常運營管理: 組織建設、培訓、工作佈達及考核, 確保工作按照規劃100％完成 

2.人力需求評估與規劃, 招募評估及風險因應, 人員管理及KPI提升改善 

3.負責新產品試產方案評估、驗證規劃、新設計驗證改善 

4.維修分析團隊建設，人力佈局與Local接班梯隊培養 

5.維修方法驗證導入與良率提升改善

1. 開發人形機器人之雙臂／雙足運動控制與任務決策模型 

2. 設計 PD / Impedance / Model Predictive Control 等控制器 

3. 訓練強化學習與模仿學習模型，實現決策與感知行為 

4. 使用 Isaac Sim / Gazebo 等模擬平台驗證 Sim2Real 效果 

5. 整合 AI 模型與控制模組，執行實體部署與系統測試

- 推動跨系統和模塊的集成，解決架構相關技術問題 

- 架構設計: 設計Genesis Store 架構模塊並與IT/OT組件集成 

- 與開發團隊保持密切溝通交流，設置檢查要求和節點，推動用例標準化 

- 生態合作對接: 確定IT/OT架構與生態解決方案之間的一致性和匹配性 

- 知識庫與Token*相關的系統建設: 負責相關的系統設計與建設 (如: 知識管理、Token*管理) 

*Token: 透過GenAI技術整合各種跨事業群的生產相關資料，如: 語句、圖像後的數據單位，有助在不同場景下訓練AI引擎

Cable 設計、系統階設計、BOM規劃、組裝規劃、Label 規劃、FRU 規劃

1. 制定自動化設備視覺應用改進完善方案 

2. 視覺應用工作整體把控 

3. 機器視覺方案設計及可行性分析 

4. 自動化機器視覺算法設計及開發 

5. 與客戶檢討設備方案及驗證狀況改善推動 

6. 有良好的帶隊能力或專案驅動力

【工作內容】 

1.資訊機房維運管理，熟悉虛擬主機ESXi管理、維護與備份。 

2.伺服器、網路規劃、維護與管理。 

3.系統事件處理、日誌監控分析與管理。 

4.資訊安全管理系統執行及持續改善。 

5.支援IT專案任務及主管交辦事項。 

6.熟悉電腦與伺服器硬體與規格，TCP/IP網路、防火牆、Router、Switch、VPN。 

7.Kubernetes/Openstack/Red hat 管理、維護與備份。

【工作職責】 

•需求分析與評估： 透過諮詢與觀察，確認商業需求，評估現有 IT 架構並提出改善建議。 

•系統開發與配置： 開發、配置客製化 SAP 解決方案，確保系統整合順暢。 

•程式撰寫： 撰寫腳本與程式，建立友善的使用者介面並優化功能。 

•支援與優化： 排除故障以優化效能，並提供系統支援與終端使用者教育訓練。 

•文件與合規： 執行嚴謹的 SAP 流程文件記錄，確保符合產業規範，並持續掌握 SAP 最新技術。 

【Responsibilities】 

•Analysis & Assessment: Determining business needs via consultation and targeted observations; assessing existing IT infrastructure and recommending improvements. 

•Development & Configuration: Developing, configuring, and deploying customized SAP solutions while ensuring smooth system integration. 

•Scripting: Writing scripts and programs to create user-friendly interfaces and enhanced functionalities. 

•Support & Training: Troubleshooting issues to optimize performance, providing SAP system support, and conducting end-user training. 

•Documentation & Compliance: Performing rigorous documentation of SAP processes, presenting progress reports, and ensuring compliance with industry regulations.

參與伺服器 BMC/BIOS 韌體功能的編寫、修正與模組化測試；協助開發系統監控與維運管理工具。配合電磁相容 (EMC) 實驗室進行初步測試計畫執行，支援針對不同處理器架構的驅動程式移植與核心效能初步驗證，學習韌體安全性更新流程。 

Participate in writing, fixing, and module testing of server BMC/BIOS firmware. Assist in developing system monitoring and maintenance tools. Support initial EMC testing plans and assist in driver porting and core performance validation for various processor architectures.

協助設計高效能伺服器與超算機櫃之主板、CPU/GPU 載板電路圖。參與先進運算架構之硬體原型功能測試、零件選型與初步 BOM 製作。配合資深工程師學習高速訊號完整性 (SI/PI) 模擬分析技術，支援液冷散熱機構的初步設計驗證與電源傳輸網路測試。 

Assist in designing circuit diagrams for AI server mainboards and CPU/GPU carrier boards. Participate in functional testing of hardware prototypes for advanced computing architectures. Support part selection and initial BOM creation. Collaborate with engineers to learn SI/PI simulation and support initial liquid cooling design and PDN testing.

參與電腦視覺與 NLP 技術研發，協助開發領域專屬模型。配合設計 AI 應用框架，支援將生成式 AI 導入實際製造場景；協助執行數據標註與推理效能評估。 

Participate in CV and NLP R&D; Assist in domain-specific model development. Support AI application frameworks and GenAI implementation in manufacturing; Assist in data labeling and inference evaluation.

參與智慧醫療產品與穿戴式生理量測設備的需求分析、功能設計與產品原型測試。協助開發生醫雲端平台之應用層功能，並支援生理訊號處理演算法的初步驗證。配合推動 AI 護理協作方案於醫療場域的測試佈署，支援遠距照護平台的數據串接與日常維護作業。 

Participate in requirement analysis and prototype testing for medical wearables; Assist in developing biomedical cloud platform functions. Support AI nursing solution deployments and data maintenance.

＜About the Job＞ 

We are looking for a highly motivated and skilled AI Infrastructure Engineer with strong hands-on experience in Kubernetes (K8s), particularly in supporting AI/ML workflows. In this role, you will be instrumental in designing, implementing, and maintaining robust, scalable, and high-performance Kubernetes-based infrastructure that supports the entire lifecycle of our AI applications—from data processing and model training to deployment and monitoring. You will work closely with data scientists, AI/ML engineers, and DevOps teams to ensure seamless integration of AI/ML workloads within cloud-native environments. The ideal candidate has a deep understanding of container orchestration, distributed systems, and MLOps practices, and is passionate about building efficient, reliable platforms that enable rapid AI innovation. This is a unique opportunity to work at the intersection of AI and cloud infrastructure, contributing to next-generation systems that power intelligent applications at scale. 

＜Job Responsibilities＞ 

．Design & Architecture: Design, build, and scale a reliable and efficient Kubernetes platform optimized for AI/ML workloads. This includes provisioning GPUs, managing resources, and ensuring optimal performance for computationally intensive tasks. 

．Infrastructure Management: Manage the entire Kubernetes cluster lifecycle—from provisioning and configuration to ongoing maintenance, monitoring, and troubleshooting, ensuring high availability and scalability. 

．Deployment & Automation: Develop and implement CI/CD pipelines to automate the deployment, scaling, and updating of machine learning models and AI services. Ensure seamless integration with AI tools like Kubeflow, MLflow, and Argo Workflows. 

．Performance Optimization: Continuously monitor and optimize system performance, focusing on resource utilization, latency reduction, and improving the overall efficiency of AI workloads. Ensure high availability and minimal downtime for AI services. 

．Collaboration & Guidance: Work closely with data scientists, ML engineers, and cross-functional teams to understand their infrastructure requirements and provide technical solutions to meet workload demands effectively. 

．Security & Compliance: Implement best practices for cluster security, including network policies, access controls, and vulnerability management to safeguard sensitive data and maintain compliance. 

．Cost & Resource Efficiency: Manage resources effectively to optimize cost while maintaining high-performance infrastructure for AI model training, inference, and data processing. 

＜Skills & Qualifications＞ 

．Kubernetes Expertise: You should have hands-on experience with Kubernetes (K8s) architecture, including deploying applications, managing resources, and troubleshooting complex cluster issues in a production environment. 

．Containerization & Linux Environment: Strong knowledge of container technologies such as Docker, along with hands-on experience in Linux environments. Expertise in container orchestration and deployment practices is highly valued. 

．AI Workloads: Deep understanding of GPU scheduling and performance optimization, including strategies for resource allocation, workload balancing, and maximizing throughput for AI/ML tasks. 

．Automation & CI/CD: You need practical experience with building and managing CI/CD pipelines using tools like GitLab CI, Jenkins, GitHub Actions, or ArgoCD to automate deployments. 

．Programming & Scripting: Proficiency in at least one scripting language (e.g., Python, Bash) is a must. 

．Networking: Knowledge of container networking and service mesh technologies (e.g., Istio, Linkerd) is highly desirable and a great advantage.