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AI Technology EngnieerAI 技術 工程師 此職位的主要義務和職責 Listing of the key duties and responsibilities performed by the job 1. System Development: a. Design, develop, and deploy CV and RPA algorithms and systems to semiconductor assembly quality control and automation. b. Develop and integrate generative AI models to generate, simulate, or optimize manufacturing processes. 2. System Quality Assurance: a. Implement real-time image analysis systems to detect, classify, and track defects in semiconductor components and assemblies. b. Utilize AI techniques to analyze data and provide predictive insights for quality assurance. 3. Data Analysis and Model Training: a. Analyze visual and operational data to identify trends, patterns, and areas for improvement in manufacturing processes. b. Train and optimize machine learning models using extensive datasets to enhance accuracy and performance. 4. System Integration: a. Integrate computer vision and AI solutions with existing manufacturing systems and platforms to ensure seamless operation. b. Collaborate with hardware engineers to ensure compatibility and robustness of computer vision setups. 5. Documentation and Reporting: a. Create detailed technical documentation for developed systems, including specifications, user guides, and training materials. b. Present findings, recommendations, and performance metrics to management and relevant stakeholders clearly and compellingly. 6. Continuous Improvement: a. Stay abreast of advancements in computer vision, generative AI, and manufacturing technologies. b. Propose innovative solutions and process enhancements to maximize efficiency and product quality. 1. 系統(tǒng)開發(fā)： a. 設計、開發(fā)和部署 CV 和 RPA 算法和系統(tǒng)，用于半導體組裝質量控制和自動化。 b. 開發(fā)和集成生成式 AI 模型，以生成、模擬或優(yōu)化制造流程。 2. 體系質量保證： a. 實施實時圖像分析系統(tǒng)，以檢測、分類和跟蹤半導體元件和組件中的缺陷。 b. 利用 AI 技術分析數(shù)據并提供預測性見解以保證質量。 3. 數(shù)據分析與模型訓練： a. 分析視覺和運營數(shù)據，以確定制造流程中的趨勢、模式和需要改進的領域。 b. 使用廣泛的數(shù)據集訓練和優(yōu)化機器學習模型，以提高準確性和性能。 4. 系統(tǒng)集成： a. 將計算機視覺和 AI 解決方案與現(xiàn)有的制造系統(tǒng)和平臺集成，以確保無縫運行。 b. 與硬件工程師合作，確保計算機視覺設置的兼容性和穩(wěn)健性。 5. 文檔和報告： a. 為開發(fā)的系統(tǒng)創(chuàng)建詳細的技術文檔，包括規(guī)格、用戶指南和培訓材料。 b. 清晰而令人信服地向管理層和相關利益相關者展示調查結果、建議和績效指標。 6. 持續(xù)改進： a. 緊跟計算機視覺、生成式 AI 和制造技術的進步。 b. 提出創(chuàng)新解決方案和工藝改進，以最大限度地提高效率和產品質量。 任職資格Qualifications 教育背景Education 1. Master’s degree in computer science, Electrical Engineering, Robotics, AI, or a related field.計算機科學、電氣工程、機器人技術、人工智能或相關領域的碩士學位。 知識Knowledge *職位要求的信息或知識范圍 Statement of the Informational or conceptual framework required by the Job 1. Proficient in programming languages such as Python, C++, or Java relevant to computer vision and AI applications. 2. Experience with computer vision libraries (e.g., OpenCV, TensorFlow, Keras) and generative AI tools (e.g., GANs, VAEs). 3. Understanding of machine learning algorithms, especially in image processing and automated decision-making. 1. 精通與計算機視覺和 AI 應用相關的編程語言，如 Python、C++ 或 Java。 2. 具有計算機視覺庫（例如 OpenCV、TensorFlow、Keras）和生成式 AI 工具（例如 GAN、VAE）的經驗。 3. 了解機器學習算法，尤其是在圖像處理和自動決策方面。 相關工作經驗Experiences Experienced candidates, Semiconductor Engineering working experience is a plus. Experience in computer vision applications and Gen-AI development or a related field. Familiarity with generative AI frameworks and methodologies Relevant certifications in computer vision, artificial intelligence, or machine learning (AWS Certified Machine Learning, Google AI certification) are a plus. 有經驗的候選人，有半導體工程工作經驗者優(yōu)先。 具有計算機視覺應用程序和 Gen-AI 開發(fā)或相關領域的經驗。 熟悉生成式 AI 框架和方法計算機視覺、人工智能或機器學習方面的相關認證（AWS 認證機器學習、Google AI 認證）是加分項。 其他勝任力Competencies &Soft Skill 1. Good English skill, both written and oral 2. Good teamwork spirit Strong self-learning motivation, good communication skill. 1. 良好的英語書面和口頭表達能力 2. 良好的團隊合作精神 較強的自學動機，良好的溝通能力。