轉職熱搜工作

1.負責設備方案與規格洽談、成本評估與報價。 2.設計與優化自動化設備及治具。 3.進行圖面繪製與BOM 表製作。 4.執行自動化硬體相關專案。 5.協助產線設備異常分析與處理及新舊設備架線與調試。 6.撰寫、維護及修訂設備規範與相關技術文件。

1.次世代HIS系統微服務設計與開發： 負責次世代HIS系統雲端微服務設計與拆分。 進行核心業務模塊的後端微服務API開發與接口設計，實現前後端分離架構的前端界面API開發與交互功能。編寫技術文檔、設計文檔及API接口文檔。 2. 數據湖架構設計與搭建： 結合 HIS、EMR、PACS 等醫療系統需求，設計智慧醫院及機器人平台高可用資料庫架構；選型關係型 / 非關係型資料庫，搭建優化集群。 3. 數據集成與支持： 參與數據中臺建設，完成 ETL 數據整合；支持開發團隊設計評審，為科室提供技術諮詢與培訓。開發與LIS、PACS、EMR等醫療系統的集成接口，實現HL7、FHIR等醫療數據標準的數據交換，確保醫療數據傳遞的準確性和完整性。 4. 雲平台容器化部署與運維管理 建立和維護CI/CD自動化部署流程。負責資料庫實例、許可權、性能調優；搭建監控體系（CPU、IO 等指標） 5. 跨部門協作與需求實現

1.儀器設備校準與MSA作業及巡檢排查 2.儀器設備外部校準申請與結報 3.儀器基本資料表、校正、MSA報告歸檔 4.主管交辦事項

1. 伺服器產品機構設計 2. 新產品RFQ規劃提案 3. 開發階段設計問題改善 4. 量產階段設計問題排除 5. 2D/3D/PCA圖面繪製

1. 伺服器產品機構設計專案管理 2. 熟悉新產品RFQ規劃至MP 3. 產品技術管理, 具有客戶溝通檢討經驗 4. 會議跟進與問題管理與追蹤

1. 電子產品組裝/包裝流程確立以及製程優化，點膠/鐳雕/超聲波壓合/氣密測試等製程驗證及改善。 2. 治工具設計方案評估及review，驗收及問題點改善。 3. 產品組裝包裝生產異常分析及對策樹立。 4. ME團隊管理與能力提升，工程師分析難點指導，工程師報告review。 5. 客戶稽核以及客訴應對。 6. 主導新產品/新線體/新廠建立或產品廠別移轉的組裝包裝製程認證。 7. PFMEA/DFM/FA/DOE/MSA等報告製作

＜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.

＜About the job＞： The Data Science & AI team of headquarter IT is developing the frontier and practical analytic technologies that enhance the data value. As the data scientist, you‘ll join the AI/Big Data Analytics program/projects related to management topics, including Commercial/Industrial Engineering/Supply Chain/Financial Performance/Operation...etc., to build the model or algorithm to empower data-driven & analytics-driven for driving business value from data insights in this world-class company (Fortune Global 500, 22nd). ＜Job Description＞： ．Design, implement and refine advanced Statistical Modeling/Machine Learning/Deep Learning/Numerical Simulation/Optimization Algorithm Models(at least one of the fields) ．Ensure alignment of modeling initiatives with the requirement goal defined by key stakeholders and company objectives and identify new hypotheses for model improvements. ．Executing big data analysis and predictive analytics projects include feature engineering, model building, algorithm development, etc. ．Works closely with a team of data system analysts, business data analysts, data engineers, data platform architects, etc. ．Collaborate effectively with team members, whether leading tasks or supporting initiatives led by others. ．Self-motivated, Result-oriented, and interested in applying quantitative methods to solving business and engineering problems. ＜Skills＞ ．Experience with any one of Machine Learning, Statistical Modeling, Deep Learning(Nature Language/Image), Econometric Modeling, Optimization Algorithm(OR), Numerical Simulation..., etc., model/algorithm building of the practical application in the industry. ．Familiarity with programming languages like Python or R, or Java. (Good programming skills in Python is a plus). ．Advanced ability to perform Exploratory Data Analysis and working knowledge of statistics. ．Ability to visualize data in the most effective way possible for a given task, especially visualize models and results and debug and troubleshoot code and models. ．Big on finding the many insights based on the data and being passionate and patient about working with complex data.

＜About the job＞： The Data Science & AI team of headquarter IT is developing the frontier and practical analytic technologies that enhance the data value. As the data scientist, you‘ll join the AI/Big Data Analytics program/projects related to management topics, including Commercial/Industrial Engineering/Supply Chain/Financial Performance/Operation...etc., to build the model or algorithm to empower data-driven & analytics-driven for driving business value from data insights in this world-class company (Fortune Global 500, 22nd). ＜Job Description＞： •Design, implement and refine advanced Statistical Modeling/Algorithm Models •Ensure alignment of modeling initiatives with the requirement goal defined by key stakeholders and company objectives and identify new hypotheses for model improvements. •Executing big data analysis and predictive analytics projects include feature engineering, model building, algorithm development, etc. •Collaborate effectively with team members, whether leading tasks or supporting initiatives led by others. •Self-motivated, Result-oriented, and interested in applying quantitative methods to solving business and engineering problems. •Able to use the cutting-edge technologies to solve the practical problems ＜Skills＞ •Experience with any one of Machine Learning, Statistical Modeling, Deep Learning, Econometric Modeling, Optimization Algorithm(OR), Numerical Simulation..., etc., model/algorithm building of the practical application in the industry. •Familiarity with programming languages like Python or R, or Java. (Good programming skills in Python is a plus). •Advanced ability to perform Exploratory Data Analysis and working knowledge of statistics. •Ability to visualize data in the most effective way possible for a given task, especially visualize models and results and debug and troubleshoot code and models. •Experience in setting up supervised & unsupervised learning models including data cleaning, data analytics, feature creation, model selection, performance metrics & visualization

＜About the job＞ As the AI Project Manager and System Analyst, you‘ll join the various advanced Data Science & AI Projects in the corporate headquarters. In addition to developing intelligent applications via related AI and Advanced Data Analytics Technology for digital transformation, you will have plenty of opportunities to develop emerging AI applications based on different use cases and expand your tech skillset in this world-class company. ＜Job Description＞ *Type1：AI Project Manager Manages AI-focused projects from initial planning to delivery, ensuring alignment with business goals and promoting innovation. Responsibilities include coordinating cross-functional teams, allocating and optimizing resources, and maintaining project schedules and budgets. The role requires proactive problem-solving to address project challenges and mitigate risks while driving effective communication across departments. ＜Qualifications＞ 1. Over 5 years of experience in software project management, with a strong background in Agile methodologies. 2. Highly proactive, self-motivated, and accountable. 3. Willing and able to travel internationally for approximately 1-2 months per year. ＜Required Skills＞ 1. Strong understanding of the project lifecycle, including planning, execution, monitoring, and closure. 2. Proven ability to manage project timelines, budgets, and resources effectively, ensuring projects are completed on schedule and within budget.t. 3. Ability to communicate effectively with individuals from different departments and roles and coordinate resources. 4. Strong analytical and problem-solving skills, with the ability to swiftly assess complex issues and implement solutions. 5. Mastery of Microsoft Office programs (Word, Excel, PowerPoint) (Nice to have, but not necessary) 1. Familiarity with manufacturing processes 2. Possession of project management certifications such as PMP or Scrum. 3. Intermediate or above English proficiency in listening, speaking, reading, and writing. *Type 2：AI System Analyst Responsible for gathering and analyzing the requirements and contributing to designing, developing, testing, and deploying in all phases of the AI system development project lifecycle, as below： 1. Collaborate with business data analysts to analyze user requirements and ensure that requirements and acceptance criteria satisfy user needs. 2. Create a system requirement specification to elaborate on system design, system components/interfaces, and data schema. 3. Work with the multi-functional engineering team(e.g., data scientist, AI/ML engineer, data engineer, AI software engineer..., etc.) to ensure technical feasibility. 4. Drive usability and quality of the AI system developed and ensure all system requirements are well implemented. 5. Work on various enterprise AI system applications and problems, and analyze issues to identify root causes and prevention. 6. Create the Wireframes(or UI Prototypes) for AI system feature discussion with users and provide them to the UI/UX design team. 7. Author and update internal documentation; initiate and deliver requirement specification and system analysis/system design documentation. ＜Required Skills＞ 1. Practical experience in requirements gathering and analysis and change management 2. Experience co-working with front-end and back-end system development teams. 3. Strong analytical, verbal, and written communication, teamwork, and highly collaborative skills. 4. Experience with Microsoft Visio, Axure RP(or other wireframe tools). 5. Mastery of Microsoft Office programs (Word, Excel, PowerPoint) (Nice to have, but not necessary) 1. Mastery of QA methodology and problem-solving tools/techniques in software engineering. 2. Knowledge of AI/Data Science Tech(e.g., Machine Learning, Deep Learning, Statistics, Quantitative modeling techniques). 3. Practical experience in Relational DB (e.g., MS SQL, PostgreSQL) and ability to write SQL queries.