MLOps Engineer (VicOne Automotive Security)

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趨勢科技 - 全球雲端資安領航者 / 全亞洲最大軟體公司 / 企業版圖橫跨五大洲 / 趨勢全球研發基地在台灣 
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Role: Deploy, manage, and optimize machine learning models in production environments, ensuring seamless integration and efficient operations.

Responsibilities:

• Check deployment pipelines for machine learning models.

• Review code changes and pull requests from the data science team.

• Trigger CI/CD pipelines after code approvals.

• Monitor pipelines, ensuring all tests pass, and model artifacts are generated/stored correctly.

• Deploy updated models to production after pipeline completion.

• Collaborate closely with the software engineering and DevOps teams to ensure smooth integration.

• Containerize models using Docker and deploy on cloud platforms (AWS/GCP/Azure).

• Set up monitoring tools to track metrics like response time, error rates, and resource utilization.

• Establish alerts and notifications to detect anomalies or deviations from expected behavior quickly.

• Analyze monitoring data, logs, files, and system metrics.

• Collaborate with the data science team to develop updated pipelines to address any faults.

• Document and troubleshoot changes and optimizations.

Competencies:

• Deep quantitative/programming background in highly analytical disciplines such as Statistics, Economics, Computer Science, Mathematics, Operations Research, etc.

• 2-4 years of experience in managing machine learning projects end-to-end, with the last 6 months focused on MLOps.

• Monitoring build and production systems using automated monitoring and alarm tools.

• Knowledge of machine learning frameworks: TensorFlow, PyTorch, Keras, Scikit-Learn, or others.

• Experience with MLOps tools such as ModelDB, Kubeflow, Pachyderm, Data Version Control (DVC), or others.

• Experience in supporting model builds and deployments for IDE-based models and autoML tools.

• 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.

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