資深後端開發工程師(土城)_S

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

1. 以系統開發為solution，主導NPI工作流程的自動化與系統化，包括分析系統需求、撰寫需求文件並推進開發案的進行； 

2. 溝通部門內RD團隊工程師，以及推進跨部門的協商與合作； 

3. 收集數據並製作各類型報表、報告或簡報，如每週開發進度報告、年度開發成果報告、節約效益計劃簡報、對客戶報告(英文)等； 

4. 主持各類型會議，如開發週會、部門年度成發會、客戶會議(英文)等； 

5. 推動其他主管交辦專案，如產學合作案等。

【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

開發各種新世代寬頻通訊設備VoIP, ADSL, WLAN, Cable Modem, Ethernet、Home Lans, Internet Appliances, STB, WiMax, IP Phone, Video Phone, Smart Phone ....之軟體/韌體

1. 熟悉C#，應用於Unity開發 

2. 完整專案經驗者 

3. 完整App(iOS/Android)上架經驗 

4. 熟悉 Git指令或熟悉 SoureTree 

5. 分析需求,分析問題, 解決問題 

6. 依團隊需求建立良好程式架構

1. 通訊設備嵌入式系統研發 

2. IP network通訊協定研發 

3. 物聯平臺研發(LPWAN GW, module/sensor, IoT platform) 

4. 數據平臺開發(數據分析,演算法規劃,解決方案規劃/數據處理,資料串接/視覺化)

1、熟練掌握web開發技術，對http協議、加密解密、web安全等相關技術有深入了解優先考慮； 

2、精通springboot ，熟練掌握Java編程、SSM框架、對jvm、spring、spingMVC、mybatis源碼研究者優先考慮； 

3、熟練掌握oracle，sqlServer，Mysql的使用和開發、對sql優化有豐富的經驗；  

4、學習能力強，適應能力好；具備耐心、細心的品質、能CodeReview，具備良好的團隊溝通和協作能力；

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).