R0005892: MLOps Engineer (VicOne_Automotive Security)

Job updated about 2 months ago

Job Description

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.

Requirements

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.

1
2 years of experience required
40,000+ TWD / month
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