軟體工程師

1. 架構設計與搭建：結合 HIS、EMR、PACS 等醫療系統需求，設計智慧醫院及機器人平台高可用資料庫架構；選型關係型 / 非關係型資料庫，搭建優化集群。 

2. 數據集成與支持：參與數據中臺建設，完成 ETL 數據整合；支持開發團隊設計評審，為科室提供技術諮詢與培訓。 

3. 安全與合規：管控數據訪問許可權，加密敏感醫療數據；配合審計與監管檢查，落實合規整改。 

4. 日常運維與監控：負責資料庫實例、許可權、性能調優；搭建監控體系（CPU、IO 等指標）；制定並執行備份恢復策略。 

5. 技術迭代與應急：評估新技術（雲資料庫、分佈式等），制定升級遷移方案；制定應急預案，處置宕機、數據損壞等突發問題。

We are seeking a highly motivated Software Team Leader, who combines technical leadership, system-level thinking, and cross-functional collaboration with R&D, Operations, and global customers to enable efficient diagnostics, automation, and intelligent data integration. 

Responsibilities 

•Lead the software team to design, develop, and integrate diagnostic software solutions for AI server and storage platforms. 

•Build a unified Diagnostics OS that supports modular and reusable test components across multiple products and customers. 

•Drive automation, standardization, and integration with production lines. 

•Collaborate with R&D and Operations to enable efficient issue diagnosis and resolution throughout the product lifecycle. 

•Develop and maintain automation tools, test scripts, and diagnostic libraries for manufacturing. 

•Define and enforce standard interfaces and log formats for seamless integration with the operation flow. 

•Document diagnostic procedures, APIs, and best practices. 

•Evaluate and adopt new tools or technologies to enhance testing efficiency and data analytics. 

Qualifications 

• Strong communication and problem-solving skills, able to respond quickly to customer and factory issues from different time zones. 

• Solid understanding of server system, AI server architecture, and diagnostics workflows. 

• Experience leading software teams and coordinating with cross-functional teams for global customers. 

• Proficient in software development lifecycle management and quality assurance practices.

1.AI / Compute / Storage伺服器測試軟體開發及相關軟體模組維護(含 板階、系統階、櫃階 測試 

2.Compute / Storage伺服器壓力測試軟體開發及維護:主要為系統階測試 

3.依伺服器組件、功能、出貨規格 建立 測試計劃，完成測試軟體套件開發後導入工廠端交付測試執行團隊: 

依專案 需短期出差美國、墨西哥 (1個月/次，年累積三個月以下)

1. 使用 VHDL/Verilog 進行 CPLD 電路邏輯開發。 

2. 執行模擬、合成與晶片燒錄，並進行 Debug。 

3. 設計 I²C、SPI、UART 等數位介面，與硬體電路整合。 

4. 確保設計符合時序需求，並優化資源與功耗。 

5. 產出設計規格書/測試報告，並與韌體、製造團隊協同作業。

1.Server BMC 功能開發/除錯/驗證。 

2.配合硬體及工廠解決問題 

3.支援客戶BMC特殊規格需求更新

1.Server BMC 功能開發/除錯/驗證 

2.配合硬體及工廠解決問題 

3.支援客戶BMC特殊規格需求更新

1.AI server BIOS 功能開發/除錯/驗證以及Release BIOS WFU/LVFS package。 

2.配合硬體及工廠解決問題 

3.BIOS安全性更新和 refresh 專案開發 

4.支援客戶BIOS特殊規格需求更新

1.AI server BIOS 功能開發/除錯/驗證以及Release BIOS WFU/LVFS package。 

2.配合硬體及工廠解決問題 

3.BIOS安全性更新和 refresh 專案開發 

4.支援客戶BIOS特殊規格需求更新

1. 主導系統架構設計：規劃並設計次世代醫療系統（HIS）總體技術架構與演進藍圖。 

2. 微服務拆分與治理：根據醫院核心業務，主導微服務的邊界劃分、API 契約設計與服務治理策略。 

3. 數據架構規劃： 設計數據湖與數據中臺架構，規劃全院級的數據整合、ETL 流程與數據治理體系。 

4. 推動醫療數據標準化： 負責推動並落實 HL7/FHIR 等國際醫療資訊標準在系統中的應用與數據交換。 

5. 制定技術規範： 建立與推行開發、安全、部署、監控等方面的技術標準、規範與最佳實踐。 

6. 規劃 DevOps 體系： 設計 CI/CD 自動化流程與可觀測性（Observability）系統方案，提升交付效率與系統穩定性。 

7. 技術選型與評估： 負責核心技術元件（雲端服務、資料庫、中間件）的選型、評估與概念性驗證（PoC）。 

8. 解決方案與技術攻關： 領導團隊解決專案中的關鍵技術挑戰與架構瓶頸。 

9. 技術指導與審查： 提供技術指導與培訓，並負責核心程式碼與架構設計的審查（Review）。 

10. 前瞻技術研究： 持續引入雲端計算前沿技術，驅動醫療系統的架構創新。

1.次世代HIS系統微服務設計與開發： 

負責次世代HIS系統雲端微服務設計與拆分。 

進行核心業務模塊的後端微服務API開發與接口設計，實現前後端分離架構的前端界面API開發與交互功能。編寫技術文檔、設計文檔及API接口文檔。 

2. 數據湖架構設計與搭建： 

結合 HIS、EMR、PACS 等醫療系統需求，設計智慧醫院及機器人平台高可用資料庫架構；選型關係型 / 非關係型資料庫，搭建優化集群。 

3. 數據集成與支持： 

參與數據中臺建設，完成 ETL 數據整合；支持開發團隊設計評審，為科室提供技術諮詢與培訓。開發與LIS、PACS、EMR等醫療系統的集成接口，實現HL7、FHIR等醫療數據標準的數據交換，確保醫療數據傳遞的準確性和完整性。 

4. 雲平台容器化部署與運維管理 

建立和維護CI/CD自動化部署流程。負責資料庫實例、許可權、性能調優；搭建監控體系（CPU、IO 等指標） 

5. 跨部門協作與需求實現

1. 使用者介面開發： 負責開發高品質、高效能、響應式的醫療系統前端應用程式與操作介面。 

2. 組件化架構實踐： 設計、開發並維護可複用、可擴展的標準化前端組件庫。 

3. API 數據串接： 與後端團隊協作，串接 RESTful API 或 GraphQL 接口，實現前後端數據交互。 

4. 應用程式狀態管理： 負責複雜前端應用的狀態管理機制設計與實現（如 Redux, Zustand）。 

5. 前端效能優化： 分析並優化應用的載入、渲染與運行效能，提升使用者體驗。 

6. 確保程式碼品質： 撰寫單元測試、組件測試與端到端測試，並參與程式碼審查（Code Review）。 

7. 前端工程化建設： 維護專案的建構工具鏈（Webpack/Vite）與 CI/CD 流程。 

8. 瀏覽器兼容性與調適： 確保應用在主流瀏覽器與不同設備上的兼容性與一致性。 

9. 跨團隊協作： 與 UI/UX 設計師、產品經理、後端工程師緊密合作，將產品需求高效落地。

1. 負責雲端平台的架構設計與開發實現，支撐端雲一體化整合方案的技術落地，保障雲端系統與終端設備的高效協同及穩定運行。 

2. 參與慢病管理平台、醫院HIS系統數據通道等基礎架構的雲端技術搭建，設計並實現FHIR協議數據互通的雲端解決方案，確保全場景數據流暢傳輸與交互。 

3. 基於客戶需求，進行雲端系統的定制化開發與優化，參與解決方案及系統設計說明書中雲端部分的編寫，明確雲端應用與設備端產品的整合邏輯。 

4. 與FAE、業務團隊協作，理解客戶在端雲項目各階段的雲端需求及問題，參與解決方案的討論與制定，跟進雲端功能的開發與交付進度。 

5. 制定雲端平台的交付規格，明確技術標準與FHIR&DICOM協議接口規範，確保雲端系統交付符合客戶預期，同時配合項目團隊完成雲端部署的技術支持。 

6. 編寫雲端開發相關的技術文件，包括微服務架構設計、接口說明、開發指南及TFDA相關資安文件，參與客戶的雲端解決方案簡報，根據反饋調整雲端技術方案。

1. AI PC MCU韌體(Embedded Controller)/FreeRTOS 程式開發、更新、維護 

2. AI PC MCU韌體(Embedded Controller)/FreeRTOS 研發時程及可行性評估 

3. AI PC MCU韌體(Embedded Controller)/FreeRTOS 規格的review與分析 

4. 專案執行與相關功能團隊合作 

5. 良好的溝通協調能力 

6. 具備產品分析與問題解決能力 

7. 對工作具備熱誠 

8. 具備邏輯思考能力與抗壓性

＜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.

1. 與客戶溝通產品規格 

2. 分析測試單位提出的顯示器問題並且與客戶說明 

3. 產線指令撰寫 

4. 與廠商討論並解決測試單位的問題

1. 部門人力能力規劃 

2. Linux環境下相關軟體開發 

3. Server量產測試軟體開發

1. 部門人力能力規劃 

2. 整合軟韌體開發資源 

3. 客戶軟韌體開發對接窗口

1. 部門人力能力規劃 

2. 客戶軟韌體量產導入服務 

3. 量產後軟韌體第一線維護及工程對接

電動車類/車用電子/儲能 

1.熟悉BMS/BMU軟體架構 

2.負責各種上位機架設以及調適 

3.負責產品法規和廠規測試,驗證以及除錯  

4.協助處理客戶端問題及現場技術支援

• Security Audits & Compliance: Execute internal and external audits, including documentation reviews, on-site audits, and vendor audits. 

• Risk Monitoring & Incident Response: Monitor security risks such as theft and unauthorized access. Assist in investigations and support corrective actions to prevent recurrence. 

• Client Project Support: Assist in implementing security plans for key customer projects. 

• Develop and update various security related documents (i.e., SOPs, policies, checklists, etc.). 

• Employee Training & Awareness: Develop, update, and support the delivery of physical security training programs focused on security awareness, theft prevention, access control, and emergency response. 

• Carry out other security-related tasks assigned by management