SAP Technical Consultant (工作地：新北市/高雄市)

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.維持與客戶良好關係，協調對接客戶開發/FA需求 

2.參與部門團隊建設，管理團隊事務，進行總結沉澱，帶動團隊進步  

3.參加專案日常會議，了解專案生產狀況，需要時進行工作匯報 

4.跨部門溝通協作，推動問題解決 

5.獨立完成問題的匯總、報告和追蹤，確保問題清晰落地  

6.負責各種簡報的製作及匯報

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

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

1. 主導新產品試產階段的生產任務； 

2. 配合客戶完成各種工程驗證，試產期間與客戶會議報告試產狀況及進度； 

3. 協調內部各單位共同合作，完成生產任務，處理試產異常

【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。

1. 售前支援，解決方案Proposal規劃，協助客戶專案導入。 

2. 雲端服務相關技術學習研究，提供客戶技術顧問諮詢。 

3. 雲服務產品架構規劃、可行性分析及技術支援。。

【工作職責】 

•負責 SAP ABAP 程式與 SAP Fiori 應用系統之設計、開發與維護。 

•與功能顧問及技術團隊密切合作，蒐集並分析業務需求，提供專業技術解決方案。 

•執行系統分析、程式除錯與問題排查，確保系統穩定運作並有效解決技術問題。 

•持續關注並學習 SAP ABAP / Fiori 相關之最新技術、工具與最佳實務。 

【Responsibilities】 

•Design, develop, and maintain SAP ABAP programs and SAP Fiori applications. 

•Collaborate with functional consultants and technical teams to gather business requirements and deliver effective technical solutions. 

•Perform system analysis, debugging, and troubleshooting to identify root causes and resolve technical issues. 

•Stay up to date with the latest SAP technologies, tools, and best practices related to ABAP and Fiori development.

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.