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1、開發經驗數據分析，特別是LLM和多模式基礎模型。 2、使用深度學習框架，如TensorFlow和PyTorch的經驗。 3、使用RAG，SelfRAG，VectorDb，GraphDB的經驗。 4、使用LLM優化框架的經驗，例如VLLM、 Ex-llama、 llama.cpp。 5、使用 Docker、Kubernetes 、Triton Inference Server 或 TensorRT的經驗。 6

Role: Deploy, manage, and optimize machine learning models in production environments, ensuring seamless integration and efficient operations. Responsibilities: 1. Check deployment pipelines for machine learning models. 2. Review code changes and pull requests from the data science team. 3. Trigger CI/CD pipelines after code approvals. 4. Monitor pipelines, ensuring all tests pass, and model artifacts are generated/stored correctly. 5. Deploy updated models to production after pipeline

Join HOMEE AI, a leader in 3D AI technology revolutionizing the furniture industry, as a Senior Software Engineer. We seek experts in 3D Gaussian Splatting, Semantic Segmentation, and 3D vSLAM to enhance our spatial planning technology. This role demands proficiency in algorithm development, data optimization, and 3D model reconstruction, leveraging cutting-edge techniques for real-world applications. Key Responsibilities: - Develop and optimize algorithms for Gaussian Splatting, Semantic/Instan

置為主)，並與演算法或Data團隊合作開發相關產品和應用 - 資料集分析、資料前處理、模型測試及驗證 - ML Ops模型訓練、佈署自動化 ◎《Our requirements for this role》◎ - CS/EE相關科系，有相關工作經驗者尤佳 - 精通 Python 程式

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

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。

▸ 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.

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