BMC 韌體工程師

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. 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。

＜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.

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

1.Server性能及健康監控程式開發(Prometheus, Grafana..) 

2.Server測試自動化程式開發(Redfish, IPMI, SNMP, BMC...) 

3.Server OS 鏡像自動化開發, 訂製及部署

•開發和維護EDA應用雲服務 

•使用Python和Git進行開發和版本管理  

•利用Python和資料結構及Algorithm進行資料處理分析  

•開發和維護Dash Web/Restful API  

•使用Yolo8和sentence-transformer進行finetune應用  

•使用OpenCV進行圖像處理應用  

•使用LLM/ VLM進行語言模型應用

1. Responsible for Windows driver management for assigned projects  

2. Windows driver issue analysis 

3. Cross-team and company collaboration

＜About the job＞  

As the Senior Data Engineer or Data Architect, you‘ll join the various advanced Data Science & AI Projects in the corporate headquarters. As well as developing intelligent applications via related AI and Big Data Analytics Technology for digital transformation, you will have plenty of opportunities to develop emerging applications based on different use cases and expand your tech skillset in this world-class company (Fortune Global 500, 20th). 

＜Job Description＞ 

*Type 1：Senior Data Engineer 

Responsible for acquiring data using API, Web scraping, or other data accessing protocol/scripting and developing the ETL data pipelines, and the data aggregation systems. Using software development experience to design and build high-performance automated systems. As below: 

1. Data Processing 

(1)Data ETL(Extracting/Transforming/Loading) process engineering and querying from relational data management(such as SQL Script). 

(2)Building systematic data quality processes and checks to ensure data quality and accuracy. 

(3)Solid coding experience in Python or Java. 

2. Data Pipeline Development 

(1)Develops a data integration process, including creating scalable data pipelines and building out data services/data APIs. 

(2)Create a data processing automation and monitoring mechanism by optimizing the data pipeline process.  

(3)Experience with dataflow/workflow/management tools, such as Apache Nifi, Apache Airflow, Azkaban, etc., is preferred. 

3. Data Crawling 

(1)Build scalable tools that automate web crawling, scraping, and data aggregation from various web pages using frameworks such as Scrapy. 

(2)Accessing data from REST APIs, particularly in parsing data in disparate formats such as JSON and XML, and developing automated engineering.  

(3) (Nice to Have) Knowledge of server-based front-end/UI technologies, including Vue/React and HTML/CSS, is preferred. 

*Type 2：Data Architect 

Responsible for designing, implementing, and maintaining scalable and reusable system architectures/data architectures for complex data structures and large data in data science and AI projects. As below: 

1. Data Schema Design 

(1)Collaborate with the team to design DB/Table Schema and Data Schema. 

(2)Consolidate the requirements and use data engineering tech to design and implement a robust Data Mart.  

(3)Experience handling all kinds of structured/semi-structured/unstructured data and streaming data is preferred. 

(4)Hands-on experience with Dimensional Data Modeling(Column-based data warehouse) or NoSQL Schema design. 

2. Data Platform Architecture 

(1)Design and build data infrastructure/platform components to support complex data pipelines ingesting various data from multiple internal and external data sources and processing. 

(2)Familiar with Big Data frameworks and processing technologies, ex: Hadoop, Apache Spark, NoSQL ...etc. 

(3)Familiar with AZURE or AWS cloud data services(hands-on experience with cloud infrastructure will be a plus) 

(4)Familiarity with the Linux OS environment, the Shell Scripting, and infrastructure knowledge.  

(5)(Nice to Have)Experience with declarative infrastructure/container technologies, such as Docker and Kubernetes (k8s/k3s). (Nice to Have).

【工作內容】 

1. 系統規劃與流程改造 

2. 系統設計及程式撰寫 

3. 主流雲平台使用與環境配置管理 

4. 用戶教育訓練及問題排除 

【工作地點】 

台北土城、高雄軟體園區 

未來公司採全球化配置, 將會有許多短期出差的機會

1. 熟悉微服務平台建置、維護與管理 (Kubernetes(K8S)、Docker)  

2. 經營/維護/優化 DevOps 工具 

3. Jenkins等 持續整合/建置環境建置與維護(CI/CD)  

4. Google Test 經驗 

5. 測試報告追蹤 

6. 可自主建立已上流程系統(必要).