MLOps Engineer

Key Responsibilities Deploy machine learning models into production environments using scalable and automated deployment practices. Build and maintain model serving infrastructure for real-time and batch inference use cases. Implement monitoring frameworks to track model performance, drift, latency, data quality and service reliability. Automate model retraining pipelines in collaboration with ML Engineers and Data Engineers. Manage model versioning, deployment lifecycle and rollback strategies. Operationalise CI/CD pipelines for machine learning workflows in collaboration with Platform Engineering teams. Ensure model deployments comply with security, governance, privacy and enterprise architecture standards. Support incident management, root cause analysis and resolution of model performance issues in production. Optimise model inference performance, scalability and cost efficiency across cloud environments. Collaborate with ML Engineers, Data Scientists, Big Data Engineers and Platfo...