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 completion. 6. Collaborate closely with the software engineering and DevOps teams to ensure smooth integration. 7. Containerize models using Docker and deploy on cloud platforms (AWS/GCP/Azure). 8. Set up monitoring tools to track metrics like response time, error rates, and resource utilization. 9. Establish alerts and notifications to detect anomalies or deviations from expected behavior quickly. 10. Analyze monitoring data, logs, files, and system metrics. 11. Collaborate with the data science team to develop updated pipelines to address any faults. 12. Document and troubleshoot changes and optimizations.
Competencies: 1. Deep quantitative/programming background in highly analytical disciplines such as Statistics, Economics, Computer Science, Mathematics, Operations Research, etc. 2. 2-4 years of experience in managing machine learning projects end-to-end, with the last 6 months focused on MLOps. 3. Monitoring build and production systems using automated monitoring and alarm tools. 4. Knowledge of machine learning frameworks: TensorFlow, PyTorch, Keras, Scikit-Learn, or others. 5. Experience with MLOps tools such as ModelDB, Kubeflow, Pachyderm, Data Version Control (DVC), or others. 6. Experience in supporting model builds and deployments for IDE-based models and autoML tools. 7. Familiarity with experiment tracking, model management, version tracking, model training (Dataiku, Datarobot, Kubeflow, MLflow, neptune.ai), model hyperparameter optimization, model evaluation, and explainability (SHAP, Tensorboard). This position requires a candidate with a strong analytical background, hands-on experience in MLOps, and proficiency in deploying and managing machine learning models in production environments. The ideal candidate should have a deep understanding of monitoring systems, machine learning frameworks, and MLOps tools.
你認識趨勢科技嗎?你認識的趨勢科技是什麼樣貌?趨勢科技有什麼過人之處,讓 FBI 自動找上門?讓世界最大跨國警察組織-國際刑警組織 (Interpol) 搶著跟它合作?讓全球 50 大企業中的 45 家企業都成為它的忠實客戶?還讓國內外知名大學拿它當課堂研究案例?現在就來探索你所不知道的趨勢科技!
【 全球安全連結世界的資安領航者 】
趨勢科技是網路資安解決方案全球領導廠商,協助世界創造一個安全的資訊交換環境。我們專為消費者、企業及政府機構設計的創新解決方案,是保護護消費者、企業及政府機構安全連結世界的最佳夥伴。市佔率在全球雲端安全、全球虛擬化安全與伺服器安全皆為第一。
【 企業版圖橫跨全球五大洲 】
趨勢科技 1988 年成立於美國加州,總部位於日本東京,於 1998 年正式在日本東京證交所掛牌上市(股票代碼:4704)。自成立以來,趨勢科技以領導性的先進技術迅速在世界五大洲拓展版圖,目前全球營運據點遍及 55 個國家,共有 7,000 位員工。
【 亞洲與台灣最大的純軟體公司 】
趨勢科技在亞洲共計有近 4,000 位同仁,台灣有 1,700 位同仁,擁有超過 1,000 位以上的工程師在台灣,負責研發全球超過 3 千萬名企業、家庭與個人用戶使用的最新及最完整的資訊安全核心技術及產品,是全球的研發中心,更是亞洲與台灣最大的純軟體公司。
【 致力於實現更安全的數位世界 】
Facebook: https://www.facebook.com/tmfreshman