ML Ops Engineer

Job OverviewServe as a dedicated hybrid ML Ops / Data Ops Engineer within the specialized Miral Destination AI team, responsible for the infrastructure, deployment, monitoring, and optimization that keep Miral Destination’s AI systems and supporting data pipelines 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, including the ability to debug data pipeline issues and develop simple data pipelines when required. 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 DestinationJob ScopeBuild and maintain ML infrastructure, CI/CD workflows, and simple data pipelines that support Miral Destination AI systemsDeploy models developed by the Data Scientist – AI into reliable, scalable production environmentsMonitor model performance, data drift, data pipeline health, 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 DTDJob Essential Bachelor’s or Master’s in Computer Science, Software/Data Engineering, AI, or related field3–5 years in ML Ops, DevOps, DataOps, or ML/data engineeringProven experience deploying and operating ML models in productionPractical experience debugging data pipelines and developing simple data pipelinesStrong MLOps practices and scalable AI deploymentIntermediate data engineering skills, including pipeline debugging and simple pipeline developmentHands-on experience with Python and preferably Databricks / MLflowCI/CD, containerization (Docker/Kubernetes), and infrastructure-as-codeModel monitoring, observability, drift detection, and data pipeline monitoringCloud platforms – AWS, Azure, or GCPInfrastructure optimization for cost, latency, and scalabilityDesirable:Experience with Databricks and enterprise data platformsExperience deploying RAG / LLM solutions in production