HPC系統軟體工程師/架構師(新北/高雄)

＜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

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. 建構 x86 / ARM 平台之 Linux RTOS 執行環境 

2. 設計與整合 EtherCAT / Modbus 等通訊協定與驅動模組 

3. 串接伺服器、感測器、相機等設備通訊控制 

4. 負責控制模組與 AI 模型的整合部署與環境建置

1. 開發人形機器人之雙臂／雙足運動控制與任務決策模型 

2. 設計 PD / Impedance / Model Predictive Control 等控制器 

3. 訓練強化學習與模仿學習模型，實現決策與感知行為 

4. 使用 Isaac Sim / Gazebo 等模擬平台驗證 Sim2Real 效果 

5. 整合 AI 模型與控制模組，執行實體部署與系統測試

- 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

設計與實作智慧城市平台所需的後端API服務，確保資料安全、效能與穩定性。 

1. 熟悉RESTful API設計與OpenAPI規範 

2. 熟悉Node.js/Go/Python等語言 

3. 熟悉容器化技術與Kubernetes 

4. 熟悉CI/CD與API安全機制

開發AI模組以支援智慧城市應用場景（如客服、預測、流程優化），並進行模型部署與測試。 

1. 熟悉Python與深度學習框架（如PyTorch、TensorFlow） 

2. 具備ML/DL模型部署經驗 

3. 熟悉MLOps與雲端運算平台 

4. 有實務專案經驗尤佳

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. uFEI BIOS porting 

2. BIOS debug

CESBG-產品線Server 

1. 伺服器BIOS軟體開發及維護 

2. 客戶規格審閱 

3. 專案問題分析及解決 

1.BIOS development engineering feasility estimation  

2.BIOS product specification definition  

3.BIOS engineering specification creation  

4.BIOS development  

5.BIOS debug and mantainance

1. Responsible for Windows driver management for assigned projects  

2. Windows driver issue analysis 

3. Cross-team and company collaboration

1、具有Web程式開發經驗。 

2、會使用Javascript、CSS、HTML5 

3、有相關前端framework經驗 ex：vue.js、flutter 

4、熟悉RWD、排版、網頁架構

1. HPC/AI 網路系統設定與管理 

2. HPC/AI 網路測試與佈署程式開發 

3. HPC/AI 網路效能分析 

3. HPC/AI 網路架構規劃

1. Server platform BMC development, validation, debugging. 

2. BMC requirements implementation and issues fixed. 

3. Project handle and collaborate with customer and cross-functional teams.