AI伺服器研發專案系統工程師(SE)

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.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

- 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

- 設計AI平台的技術架構 

- 優化模型管理、語料處理和監控工具，保障平台的高效運行和穩定性 

- 協助整合第三方成熟產品完成客製化需求

【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.