IT-Infra工程主管 (土城)

負責AI模型、軟硬體基礎設施與既有資訊系統的介面對接、部署整合與效能調校，以優化AI解決方案的落地可行性與系統協同營運效率。

熟悉Infra、資安規劃管理與制度規劃推動 

具SAP、ERP、B2B系統模組，道具優化經驗 

擔任與參與工廠推動智能化、自動化等相關實務經驗 

具10年以上實務管理經驗其中有3年以上跨國團隊管理實蹟,並可配合業務成長需求出差推動專案熟英文

1. 設計與建置 HPC / AI 叢集高速網路架構（Spine-Leaf、Fat-Tree 等大規模拓撲） 

2. 具備 InfiniBand（IB）或 RoCE / RDMA 高速網路實務經驗 

3. 建構網路自動化工具與配置管理流程，降低人工操作風險 

4. 執行網路設備驗測與效能壓測，確保 AI 訓練工作負載的穩定性與吞吐量 

5. 診斷並排除複雜網路問題，建立故障根因分析（RCA）機制

主要開發產品： 

1. Enterprise Switch 

2. Data center Switch 

3. AI Switch 

4. Telecom Switch 

主要工作內容： 

1. Hardware circuit design, schematic capture, and PCB development. 

2. Board bring-up, debugging, and failure analysis 

3. Component selection and BOM management 

4. SI/PI analysis and reliability validation 

5. Cross-functional collaboration with firmware and manufacturing teams 

6. Support NPI and mass production 

7. Generate technical documentation

參與 5G/6G 通訊軟體開發與通訊協議測試。協助低軌衛星酬載之硬體整合驗證；配合團隊進行量子計算軟體實驗，支援智慧聯網架構的技術文檔彙整 。 

Participate in 5G/6G software development and protocol testing. Assist in satellite payload hardware integration. Support quantum software experiments and compile technical documentation for smart networking.

1. 參與高階網路交換機的硬體架構設計與系統規劃 

2. 設計並驗證 PCB 電路板，確保高速訊號完整性與可靠性 

3. 整合 ASIC/FPGA、SerDes、PHY 等高速晶片模組 

4. 開發與測試 100G/400G/800G Ethernet 介面及光模組整合 

5. 進行硬體 bring-up、功能驗證與效能測試 

6. 與韌體/軟體工程師合作，確保硬體與系統整合順暢 

7. 撰寫技術文件、測試報告，並支援量產與品質控管

負責產品：網路交換機/網卡 

主要職責： 

1.架設與維護自動化測試機台，確保系統穩定與可擴充性 

2.開發與維護測試腳本（Shell Script、Python），提升測試效率 

3.在 Linux 平台上進行系統安裝、配置與效能調校 

4.整合測試工具與框架，支援持續整合 (CI/CD) 流程 

5.分析測試結果，撰寫報告並提出改善建議 

6.與研發團隊合作，設計並優化測試流程 

7.支援越南工廠測試平台建置與維運（需配合出差）

＜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. 數據中心IT架構規劃 

2. 冷凍空調、電源配置規劃 

3. 確認終端客戶IT需求

- 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

1.負責數據中心與智慧工廠之弱電系統（如 CCTV、門禁、消防、網路佈線等）規劃與設計。 

2.協助弱電系統建置、安裝與驗收，確保施工品質與專案進度。 

3.撰寫技術文件，包括系統架構、點位圖表、需求書及結案報告。 

4.與承包商及供應商協調，負責現場技術支援與工程問題解決。 

5. 執行現場勘查、系統測試與故障排除，提供技術優化建議。 

6.負責將環境監控數據點（如溫濕度、電力、水流量、設備狀態等）整合並串接至 EMS（能源管理系統）或 BMS（建築管理系統），含通訊協定設定、點位配置與整合測試。 

7.配合專案需求進行技術評估、設備選型與報價分析。

- 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