交大專區

1. Familiar with rack-level liquid cooling system configuration (L-to-L / L-to-A) 2. Experience on the cooling parts like Fan/pump/heatsink/cold plate 3. Experience on CFM simulation tool and is plus 4. Experience on facility cooling system / thermo-fluid test equipment design is plus

1.後端資料平台程式開發與設計。 2.雲端應用服務以及API功能開發、部署與運維。 3.GenAI、Agent等相關人工智慧應用程式開發與設計。 4.開發單元測試與整合測試，並能自動化相關流程 5.具有CI/CD實務經驗及容器化技術（Docker）知識尤佳

1.做為產品開發領導角色，負責指導團隊實踐DevOps方法論並解決技術或流程問題。 2.領導雲端、GenAI產品的開發、設計、測試及商業化，確保產品符合市場需求。 3.與產品經理、UI/UX團隊合作執行產品路線圖，協調跨部門資源以完成產品開發。 4.持續產品效能監控，進行數據分析並提出優化建議。

1.數據監控Web程式開發與設計 2.雲端應用服務功能開發 3.使用者介面程式開發 4.軟體模組開發與整合 5. 具有CI/CD實務經驗及容器化技術（Docker）知識尤佳

- 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. 使用示波器,並進行伺服器主機板上的高速與低速訊號品質量測與驗證 2. 整理測試數據並產生測試報告 3. 偕同設計團隊各部門同仁完成伺服器專案的開發與設計工作

1. 機器人系統之電控元件選型評估與整合 2. 規劃與設計電氣佈線架構 3. 製作相關電路圖與接線圖

1.與國際客戶溝通模治具功能與規格 2.設計圖繪製並與加工單位檢討加工可行性 3.利舊與功用性能評估 4.成本與整體製程效益評估 *需出差

1. 待測系統架設/軟體/韌體安裝/升級 2. 伺服器(L10) PSU 輸出輸入電氣信號測試 3. 伺服器機櫃 Power shelf 輸出輸入電氣信號測試 4. 製作測試報告

1,對維修區整體KPI負總責; 2.維修Attrition Cost管控; 3.維修OPE達成管控; 4.維修人力及產能規劃與達成.

【工作職責】 1. LLM訓練:根據不同車載應用場景對LLM進行微調(finetune)，包含使用參數高效微調(PEFT)與指令微調(Instruction Fine-tuning)方式，以提升模型效能與準確性。 2. LLM優化整合:將 LLM 模型整合至車載ECU，優化整合後提高AI服務正確性/準確度，用Perplexity評估 3. 本地與雲端部署:根據應用需求，選擇在本地端或雲端環境進行部署與優化 【加分項專業技能】 1. 數據處理與管理:熟悉即時數據流處理技術，如 Kafka / MQTT，可優化 LLM 在車載場景的即時推理能力。 2. 雲端部署（AWS 雲端架構：AWS Lambda（serverless）、AWS CDK 或 Terraform、IAM、網絡設置、S3）和本地部署（ AI Server/NVIDIA GB10…等） 3. RAG 技術與資料檢索強化應用 熟悉 Retrieval 相關技術（向量資料庫 / 全文檢索 / 傳統搜尋引擎檢索概念）,實作過向量索引更新、長文文件切片（Chunking）策略,有多語系文本檢索、圖片辨識、表格、非結構化資料處理經驗 4. AI 應用或其他整合技術, 熟悉 Agentic AI / 多智能體（Multi-Agent）/ MCP / A2A 架構,瞭解解將語音、影像、表格等不同資料類型與文字模型整合的方法

1. 穿戴式設備新產品導入階段硬體研發和測試驗證 2. Touch sensor, Force Sensor, 電子傳感模組組裝, 結構及性能驗證 3. 負責Touch sensor, Force Sensor, 電子傳感模組失效分析與改善方案 4. 具備英文溝通能力以與客戶技術研討 5. 配合業務出差至美國協助客戶設計開發和測試驗證 6. 因業務需求可能要出差或常駐海外

電動車類/車用電子/儲能 1.BMS/BMU 硬體電路設計 2.產品測試驗證及除錯 3.產品故障分析及8D報告撰寫 4.協助產線測試規劃及工廠生產現場支持，不良板問題分析 5. BMS客戶問題溝通及現場技術支援

1、電路圖Design review 2、Layout review 3、設計驗證 Singal Integrity & Power Integrity 4、主板失效分析和設計優化 5、系統失效分析及設計優化

1.AI PC系統散熱設計、風扇控制與噪音管理。 2.系統模擬分析、系統熱區分析、氣流場分析與流道設計。 3.系統散熱模組單體設計規格制訂、性能及可靠度驗證方法設置、issue debug等。 4.系統Thermal & Acoustic測試規劃

1. HP DM PC的射頻設計與測試管理 2. 跨部門溝通測試問題，整合射頻測試計畫 3. 負責和客戶溝通射頻設計與測試相關工作

＜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. 指導並執行客戶端設備安裝、架設與維護 2. 負責現場需求溝通，協調並解決技術問題 3. 協助FAE團隊建置，並負責海外專案人力調配

1. 規劃及執行 AI Server or NB/Compute Node/GPU 等之系統整合測試。內容包含硬體/軟韌體/相容性/效能/産品功能等。 2. 產品品質問題追蹤、分析及改善。 3. 依照專案任務需求和客戶開會報告。

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