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分析(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 pipeline

NVIDIA’s Solution Architecture team is looking for a Data Science focused Solution Architect with expertise in Machine Learning (ML), Deep Learning (DL) and Data Science platforms. We work with the most exciting computing hardware and software, driving the latest breakthroughs in artificial intelligence. We need individuals who can enable customer productivity and develop lasting relationships with our technology partners, making NVIDIA an integral part of end-user solutions. We are looking for

Understand business objectives of customers and eliciting requirements. Formulate real world problems into corresponding solvable abstract representations. Collect, process and cleanse raw data from a wide variety of sources. Evaluate the practicality of SOTA ML/DL algorithms and implement corresponding methods using real-world data. Test-Driven Development (TDD) throughout the development process. Integrate machine learning product into PEGAAi. Track product performance in the field and drive c

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。

讓使用者依賴這個產品。 工作內容： 為因應AI團隊創建，與機器學習經理合作，需有2C端品服務經歷。 ML應用架構設計與實作 從無到有落地NLP、CV演算法，建立線上服務。 依據市場需求，調整或提出state of

We devoted to helping customers leverage the value of artificial intelligence (AI) to solve business pain points. We continue to research and develop AI and data-driven intelligent analysis modules for multiple solutions, covering 3 major sub-fields of AI / ML including natural language processing, computer vision, and data science to empower customers embark on AI-driven decisions, respond to market changes faster and develop new business competencies. .

緊加入我們一起探索雲計算吧！ 工作職責： - 通過 CloudWatch 構建 AWS 各種 Managed Services 的健康監控體系。 - 基於閾值或者 ML 對不同的 Metric 進行監控，通過 Slack 報警處理。 - 通過 X-Ray 以及其他的組件為架構在 AWS 中的整體服務實

📈穩定發展，打造全球資訊安全解決方案 🕵🏻領先業界，產業領頭羊 🦉與頂尖開發成員合作，增進開發實力 【職務內容】 通過 CloudWatch 構建 AWS 各種 Managed Services 的監控體系。 基於閾值或者 ML 對不同的 Metric 進行監控，通

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

Google welcomes people with disabilities. Minimum qualifications: Bachelor's degree in Computer Science, related technical field, or equivalent practical experience. Experience collaborating with various teams such as developers, test engineers, and product management. Preferred qualifications: Experience applying AI/ML in automation or data analytics use cases. Experience developing infrastructure and tools to streamline and scale operational workflow. Experience developing back-end services or