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【Responsibilities】 1. Create and maintain optimal data pipeline architecture, assemble large and complex datasets to meet functional/nonfunctional business requirements. 2. Identify, design, and implement internal process improvements: automating manual processes, optimizing data delivery, redesigning infrastructure for greater scalability, etc. 3. Create data tools for analytics and data scientist team members that assist them in building and optimizing our product into an innovative industry l

▸ Developing new features and enhancing existing features for Computer Vision AI services. ▸ Collaborating with backend developers, data scientists, quality assurance, and project managers to develop elegant solutions. ▸ Refactoring legacy codebase, fixing bugs, and optimizing performance continuously. ▸ Writing readable, re-usable code and conducting peer review to ensure quality. ▸ Contributing ideas for UI/UX and product design.

▸ 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

歷登錄網址: Link [Summary] Winbond is building a data intelligence team to increase productivity, data capability, and business impact. This team is in charge of enterprise data platform, AI and BI solutions to empower not only business operation but also strategic decisions. We are looking for Data Engineer/Analyst to join this new and world-class data intelligence team. You'll collaborate with other Data Scientists, Data Engineers, and various domain-subject ex

Infocast is looking for a data engineer to help optimize, expand, test, and maintain the data pipeline architecture across all of our databases and for our upcoming products. You will be working closely with our software engineers and data scientist to understand and implement database requirements, troubleshoot issues, and improve data reliability and quality. The ideal candidate should have a solid understanding (both theoretical and practical) of machine learning techniques, and be comfortabl