MlOps Engineer

MLOps EngineerLocation: Dallas TX(Onsite)About the RoleWe are seeking an MLOps Engineer to bridge the gap between data science and production systems, ensuring that machine learning models are deployed, monitored, and maintained at scale. You will work closely with data scientists, data engineers, and software developers to design and implement automated, reliable, and secure ML pipelines from development to production.Key ResponsibilitiesModel Deployment & ServingDeploy ML models into production environments using tools such as Docker, Kubernetes, and model serving frameworks (e.g., TensorFlow Serving, TorchServe, MLflow).Implement CI/CD pipelines for ML workflows.Pipeline Development & AutomationBuild and maintain end-to-end machine learning pipelines for data ingestion, preprocessing, training, validation, deployment, and monitoring.Automate model retraining and versioning to ensure continuous improvement.Monitoring & MaintenanceSet up monitoring and alerting systems for model performance, data drift, and infrastructure health.Troubleshoot and resolve model degradation issues in production.Collaboration & IntegrationCollaborate with data scientists to transition models from experimentation to production-ready systems.Work with DevOps and cloud teams to ensure ML workloads are scalable and cost-efficient.Security & ComplianceEnsure compliance with data governance, security, and privacy regulations.Manage role-based access control (RBAC) for ML infrastructure.