We are seeking an experienced and highly motivated MLOps Engineer to join the Data and AI team. In this role, you will bridge the gap between machine learning development and scalable production systems by building, automating, and managing end-to-end ML pipelines. The ideal candidate will have strong expertise in cloud platforms, CI/CD automation, infrastructure-as-code, and productionizing machine learning models in enterprise environments.Key ResponsibilitiesDesign, build, and maintain scalable ML infrastructure and CI/CD pipelines for training, testing, deploying, and monitoring machine learning modelsAutomate model versioning, deployment, rollback strategies, and environment management across staging and productionCollaborate closely with Data Scientists and Machine Learning Engineers to productionize ML models and optimize deployment workflowsApply Infrastructure-as-Code (IaC) practices to provision and manage cloud-based ML infrastructureImplement monitoring, logging, and alerting solutions for ML systems, including model drift and data anomaly detectionOptimize the performance, scalability, and reliability of model training and inference systemsEnsure ML operations adhere to organizational security, compliance, and reliability standardsMaintain comprehensive documentation for systems, workflows, processes, and operational proceduresSupport continuous improvement initiatives related to MLOps, DevOps, and AI/ML operational practicesRequired QualificationsBachelor's Degree in Computer Science, Engineering, or a related field3+ years of experience in MLOps, DevOps, or Machine Learning EngineeringHands‑on experience with Azure DevOps and Azure Machine Learning (AzureML)Proficiency with cloud platforms such as AWS, Azure, or GCPExperience with containerization and orchestration technologies including Docker and KubernetesStrong programming skills in Python, Bash, and PowerShellExperience working with REST APIsExperience with Infrastructure-as-Code tools such as Terraform or ARM templatesFamiliarity with CI/CD tools including Jenkins, GitHub Actions, or Azure DevOps PipelinesHands‑on experience with machine learning frameworks such as TensorFlow, PyTorch, and Scikit‑learnFamiliarity with ML tools such as MLflow, TFX, DVC, or KubeflowExperience with workflow orchestration tools such as Apache Airflow or PrefectStrong troubleshooting, analytical, and problem‑solving skillsExcellent verbal and written communication skillsPreferred QualificationsExperience with monitoring and logging tools such as Azure Monitor, Prometheus, or GrafanaExperience working in Agile development environmentsExperience collaborating with Data Science and Machine Learning teamsStrong collaboration skills with experience working in cross‑functional technical teamsExperience optimizing scalable AI/ML operations and infrastructureCertificationsCertified Kubernetes Administrator (CKA) or equivalent certification#J-18808-Ljbffr