轉職熱搜工作

職務說明： 作為生成式 AI 工程師，你將運用生成式 AI 技術解決實際問題，負責開發與維護應用於醫療場景與行政流程的各類服務。你將參與從應用系統設計、模型整合，到服務部署的完整開發流程，推動生成式 AI 在實務場域的落地應用。 同時，也將運用提示詞設計（Prompt Engineering）技巧，協助優化大型語言模型（LLM）的輸出效果，提升內容的品質與準確性。 工作內容： 1. 擴充與維護現有 AI 平台服務及相關應用。 2. 協助現有系統與 AI 應用進行初步且深入的整合。 3. 運用提示工程相關專業，設計、建立與優化大型語言模型（LLM）的提示詞。 4. 進行生成式 AI 應用的整合與開發工作。 5. 開發與維護基於 RAG 架構的聊天機器人應用。 必要條件： 具備一年以上 .NET 或 python系統開發經驗 熟悉 MS SQL 具備LLM相關應用開發的實務經驗

● 主要職責 1. 開發及部屬醫學影像與數據AI模型 - 影像與數據資料集管理、分析、前處理 - 協助影像標註 - 模型訓練與優化 - 模型效能驗證測試 - 協助模型部屬與串接 2. 撰寫計畫書與期刊論文之方法與技術 3. 其他主管交辦事項 ● 必要條件 1. 熟悉Python或Matlab 2. 熟悉深度學習、機器學習知識 3. 熟悉影像標註方法與工具 4. 有影像分類、偵測或分割模型訓練經驗 ● 加分條件（依重要性排序） 1. 熟悉Docker 2. 有成果發表經驗（研討會、期刊論文、產品展示等） 3. 熟悉PACS、RIS、HIS系統 4. 有生物醫學相關背景 5. 有醫院工作經歷 7. 能開發多模態模型，例如檢驗數據、生理訊號、醫學影像、文本等 ** 面試時請提供個人或團隊開發專案的成果或經驗分享

職缺一*1 產品設計工程師： 1. 新樣品開發評估 2. 產品規格審查確認 3. 流程BOM撰寫 4. FPC產品排版與流程設計 5. 治工具資料制作 6. 樣品追蹤與異常改善 7.熟悉AutoCAD 2D 職缺二*1 軟體工程師： 工作內容： a.開發/維護/測試設計軟體(InCAMProFlex、InPlanFlex)自動化腳本. b.電腦系統、軟體異常處理. c.主管交辦軟體優化事項. d.伺服器、電腦周邊設備維運. 需具備能力： a.程式語言設計(C shell、vb.net、python、perl). b.Oracle(SQL/PL-SQL)資料庫. c.Windows、Linux OS.

【工作內容】 1. .NET系統軟體程式設計與開發 2. 配合需求進行系統開發&測試時程安排 3. 配合公司需要，開發/維護適用之系統，優化相關作業，加強工作效率 【必要條件】 1. 技術語言和框架開發軟體系統的經驗 (C#, MVC, .NET Framework, .NET Core, T-SQL,具Store Procedure使用經驗) 2. 主動、積極，負責任感，重視團隊溝通合作 【工作待遇】 1.營利共享，具競爭力的年薪(含員工紅利、三節禮金) 2.完善升遷/輪調制度 3.團體保險保障 4.在職訓練 5.享有每年調薪 【加分條件】 1. 具備資料庫設計和維護概念。 2. 具備系統分析、軟體設計能力者佳

我們非常重視專業的資訊人材，提供良好的工作環境以及學習與成長空間。歡迎優秀的朋友們一起加入我們的AI團隊，共同創造屬於自己的未來。 1. 參與 AI 功能的前端實作，包括大型語言模型(LLM)、影像辨識、分析預測模型等應用整合 2. 使用React框架開發與維護廠內AI相關系統 3. 開發靈活高效的UI組件，優化前端架構和性能，以及用戶使用體驗 4. 與後端工程師協作，實現產品需求與功能 5. 串接RESTful API，整合後端AI相關數據資料 6. 針對不同裝置實現RWD響應式設計，確保跨平台兼容性

1.使用先進專業電腦軟體進行設計模擬和結構分析，確保設計的可靠性和可行性。 2.應用CAM技術生成數控代碼，並設定加工參數以確保製造過程的精確性和效率。 3.根據不同的加工需求選擇合適的刀具，並設定加工深度和方式。 4.跨部門溝通與協調提供技術支援，解決製造過程中的技術問題。 5.優化設計和製造流程，提高製造工作效率和產品質量。

1. Manage team 2. BIOS development planning and management 3. Across function teams communication 4. Customer relationship maintain 5. Cultivate talents

＜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.