ML Ops Engineer

Job OverviewServe as a dedicated ML Ops Engineer within the specialized Miral Destination AI team, responsible for the infrastructure, deployment, monitoring, and optimization that keep Miral Destination’s AI systems running reliably in production.Build and maintain the pipelines and platforms that take models developed by the Data Scientist – AI from experimentation to scalable, secure operation. Report to the Senior Manager AI to ensure all operational work supports the destination’s AI roadmap and delivers dependable, performant AI services for Miral Destination.Job ScopeBuild and maintain the ML infrastructure and CI/CD pipelines that support Miral Destination AI systemsDeploy models developed by the Data Scientist – AI into reliable, scalable production environmentsMonitor model performance, data drift, and system health, and resolve operational issues for Miral Destination AI servicesOptimize infrastructure for cost, latency, and scalability across Miral Destination workloadsProvide ongoing operational support and incident response for production Miral Destination AI systemsAutomate retraining, versioning, and release workflows using Databricks and MLflowReuse enterprise platforms and shared AI capabilities, aligning with the architecture and standards set by the AI & Data organizationEnsure security, governance, and compliance standards are met across all Miral Destination AI operationsCollaborate with AI, Data Engineering, BI, and Enterprise Data teams across DTD to leverage shared capabilities, reusable assets, common platforms, and best practices.Partner with enterprise platform and data engineering teams to ensure consistency of deployment, monitoring, and operational practices across DTD.Job Essential Bachelor’s or Master’s in Computer Science, Software/Data Engineering, AI, or related field3–5 years in ML Ops, DevOps, or ML/data engineeringProven experience deploying and operating ML models in productionStrong MLOps practices and scalable AI deploymentHands-on experience with Databricks, MLflow, and PythonCI/CD, containerization (Docker/Kubernetes), and infrastructure-as-codeModel monitoring, observability, and drift detectionCloud platforms – AWS, Azure, or GCPInfrastructure optimization for cost, latency, and scalability