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的資料分析工作。 2. 開發和維護機器學習或其他相關演算法。 3. 改進機器學習模型並進行效能調校。 4. 開發並部署高度可擴展的機器學習模型。 5. 使用資料整合平台（例如AirFlow）建立MLOps流程。

析（領域資料洞悉、特徵選擇、資料前處理等） 機器學習或其他相關演算法開發, 維運 機器學習模型改善以及效能調校 開發以及部署高度可擴展的機器學習模型 使用資料整合平台（如 AirFlow）建立 MLOps 管線

▸ Participate in the entire development lifecycle of the MLOps platform ▸ Collaborate with Front-end developers to integrate user-facing elements with server side logic ▸ Collaborate with data engineers and data scientists to assist in the practical implementation of complex domain knowledgecould accelerate the ML dev cycle (MLOPs). ▸ Ability to adapt flexibly to various unforeseen and unfamiliar challenges ▸ Build reusable code and libraries for future use

▸ Participate in the entire development lifecycle of the MLOps platform ▸ Design robust and scalable systems and implement new functionality and product enhancements ▸ Identify potential system performance bottlenecks and improve them ▸ Translate vague business requirements into practical and dividable user stories ▸ Provide technical guidance to colleagues and establish a developer-friendly environment. ▸ Build reusable code and libraries for future use

Be responsible for managing our entire k8s-based infrastructure for MLOps.

detection/classification/recognition/tracking, behavior analysis, …) 和聲學處理 (Abnormal noise detection/classification, …) 3. 針對於不同的嵌入式系統的AI加速晶片(非GPU)進行模型壓縮與優化,以符合速度與準確度要求 4. 協助設計與建立MLOps流程與Data pipeline的介接工具

Roles & Responsibilities: 1. Designing and Optimizing Large Language Models: Develop and fine-tune large language models tailored for specific applications. 2. Collaboration with MLOps Engineer: Work closely with MLOps engineers to prototype custom solutions, ensuring seamless integration with existing systems. 3. AI/ML Modeling Expertise: - Demonstrate hands-on experience in AI/ML modeling, particularly with complex datasets. - Possess a strong understanding of the theoretical foundations of AI