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演算法設計、實作及相關程式撰寫。 AI模型訓練、微調、驗證及發佈。 與系統後端進行資料串接及測試。 持續關注如自然語言處理、影像處理及生成式AI技術。

演算法設計、實作及相關程式撰寫。 AI模型訓練、微調、驗證及發佈。 與系統後端進行資料串接及測試。 持續關注如自然語言處理、影像處理及生成式AI技術。

1.AI智能徵審系統管理與擴大應用。 2.規劃風險數位化專案。 3.整合風險管理及跨單位溝通協調。 4.掌握市場AI應用並規劃風險情境導入解決方案。

NVIDIA is searching for a Director to setup and build a team of Software Engineers and Data Scientists to develop artificial intelligence (AI), computer vision and data analytics algorithms and applications for our Metropolis for Factories and Manufacturing platforms. Artificial Intelligence is transforming how we collect, inspect, and analyze different kinds of sensor data that impacts everything from manufacturing automation, warehouse management, product inspections to safety workflows. NVIDI

NVIDIA is looking for Senior HPC/AI Solutions Architect to join its Professional Services team. Academic and commercial groups around the world are using NVIDIA products to revolutionize deep learning and data analytics, and to power data centers. Join the team building many of the largest and fastest AI/HPC systems in the world! We are looking for someone with the ability to work on a dynamic customer focused team that requires excellent interpersonal skills.

As a BPA/PM, you are required to support global and Japan business requirement for a variety of AI application development.

Role: AI Chatbot Engineer Job Summary: As a Chatbot Engineer, you will be responsible for developing and implementing chatbot applications for our clients. You will work closely with the development team to design, build, and maintain chatbots that meet the client's requirements. You will also ensure that the chatbot applications are scalable, reliable, and secure.

Data Organization and Analysis - Analyze the product logs and propose suggestions for product optimization. - Collect, generate, and clean data, such as finding relevant articles for specific topics and creating relevant questions. Prompting - Utilize generative AI to expedite the work process. - Develop prompts to generate data at a large scale. System Testing and Evaluation - Ensure the performance of in-house generative AI models. - Compare the performance of various language models, such as

Understanding business objectives of customers and eliciting requirements Formulating real world problems into corresponding solvable abstract representations Evaluating edge design that are feasible to deal with a given problem Developing and deploying intelligent edge product / machine learning into production Integrating intelligent edge automation / machine vision systems Tracking product performance in the field and driving continual performance improvements

Survey AI-related papers and solutions. Understand and implement the-state-of-the-art AI approaches. Cooperate with cross-functional team members. Problem Solving Capability