轉職熱搜工作

一、設計與開發 RAG 流程（Retrieval-Augmented Generation） 建置 GenAI 應用所需的 RAG 流程，包括： 　- 資料處理：轉換各種格式（如文本、圖像、程式碼）為模型可解析的結構 　- 向量嵌入：將處理後資料轉為向量表示，並儲存於向量資料庫 　- 相似度檢索：根據使用者查詢，在向量資料庫中搜尋最相關的內容 　- 提示強化：將檢索結果整合至 LLM 輸入提示中，提升生成結果品質 　- 結果產生：透過 GenAI 模型生成最終回應 評估並選擇合適的嵌入模型與向量資料庫（如 Pinecone、Milvus），持續優化流程效能與準確性 二、建置與維護向量資料庫 部署並設定向量資料庫系統，依據需求設計資料結構（schema） 開發資料導入、更新、自動化維護相關工具與腳本 監控資料庫效能與資源使用，進行調校與最佳化 三、開發與優化向量嵌入模型 根據應用場景選擇適合的嵌入模型 利用工具（如 SentenceTransformers）進行模型訓練與微調 監測模型準確性與表現，進行優化與迭代 四、提升 GenAI 應用準確性與可靠性 利用 RAG 技術降低模型產生幻覺（hallucination）的機率 確保模型生成內容具備事實性、準確性與一致性 建立監控與評估機制，強化系統回應品質 五、跨部門協作與技術溝通 與 GenAI 工程師、資料科學家、產品團隊密切合作，確保系統設計符合實際需求 以簡潔方式向非技術同仁說明 RAG 與向量技術的核心概念與應用價值 六、技術研究與持續進修 追蹤 RAG 架構、向量資料庫、嵌入技術等相關領域的最新發展 研究新穎的相似度搜尋算法與嵌入技術應用 參與社群活動、技術論壇或研討會，拓展專業視野

具備公共工程品管工程師，文書作業軟體製作報表、簡報、工程自主檢查及會同業主（監造） 查驗，無經驗著本公司可培訓 。 1.工地現場監工實務。 2.內控作業、表單處理及文書作業。 3.品管工程師執照。 4.上班工作地點：台南市新營區 聯絡方式:https://page.line.me/789dfzny

1.全球網通網路及雲網整合技術推動 2.推動鴻海廠區軟體定義網路，網路最小化，保障網路安全 3.推動鴻海廠區SASE架構，保障員工上網及遠程用戶訪問集團資源之安全 4.推動園區網路分艙隔離，建立標準，全球廠區推動規劃 5.鴻海公/私有雲之網路部署規劃及導入

1. 喇叭音質調校與優化：調整喇叭的頻率響應、音量和音質，確保聲音效果達到設計要求。 2. 喇叭設計與模擬：喇叭結構設計、材質和外形，並進行聲學模擬。 3. 測試與驗證：進行喇叭性能測試，檢查其聲音輸出和工作穩定性，並進行必要的改進。 4. 材料選擇與研究：研究並選擇適合的材料來優化聲音質量和降低成本。

1. 對於AI高階水冷工作站系統架構設計/接口定義感興趣 2. 負責物料開發及管理，確保開發流程順利 3. 制定系統測試與驗證計畫，追蹤及解決 issue 4. 跨部門協作與製程整合，處理 NPI 及量產階段的系統問題 5. 良好溝通能力與具客戶協作經驗

1.NB系統儲存模組規格確認及新技術開發引進 2.SSD & Memory產品技術發展趨勢研究 3.規劃並執行 SSD 與 Memory 的功能測試、壓力測試、相容性與壽命驗證 4.整合系統端相關 FW、BIOS、Driver 更新，以確保穩定性 5.針對 SSD / Memory 的異常現象（如無法開機、掉 disk、藍屏、bit error、read/write fail）進行除錯 6.使用示波器、邏輯分析儀、串接 debug log 或 FW log，協助分析 Root Cause 7.與 EE、BIOS、FW 團隊協作分析訊號問題、時序問題或電源供應問題

* 負責雲端Server伺服器的相關工作 1. 供應鏈系統規劃與流程改造 2. B2B系統設計程式撰寫 3. 雲平台使用與環境配置管理 4. 用戶需求訪談及常見問題管理 5. 參與客戶B2B需求討論會議 • 計畫介紹： • 實習期間: 即日起-2026/6 (長期實習) • 職缺內容: 實習生 (在學學生) (電子相關科系佳) • 實習薪資: 時薪(一週3天以上)，優於基本工資 ※完整一年期滿轉正過試用期另外有奬金哦~~

＜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. At least 5 years of experience in embedded system, FreeRTOS is a plus 2. Familiar with C/C++, Linux, TCP/IP, Streaming 3. Familiar with Reatlek, OmniVision, Sigmastar, Rockchip, Ambarella, Hisilicon or any IP camera platform. 4. Excellent troubleshooting and problem-solving skills. 5. Well communicate with different team such as Hardware, Audio, Optical, IQ, DQA, customer, etc. 6. Familiar with Git control tool

1. Lead the lead of firmware team for server project. 2. Study technical issue for project‘s needs. 3. Review & sign off requirement specification. 4. Review engineer design. 5. Review function/interface specification. 6. Review bug fixing. 7. Help the design and review the common definitions for debugging and profiling.