ML Ops Engineer

Job Overview

Serve 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 Scope

• Build and maintain the ML infrastructure and CI/CD pipelines that support Miral Destination AI systems

• Deploy models developed by the Data Scientist – AI into reliable, scalable production environments

• Monitor model performance, data drift, and system health, and resolve operational issues for Miral Destination AI services

• Optimize infrastructure for cost, latency, and scalability across Miral Destination workloads

• Provide ongoing operational support and incident response for production Miral Destination AI systems

• Automate retraining, versioning, and release workflows using Databricks and MLflow

• Reuse enterprise platforms and shared AI capabilities, aligning with the architecture and standards set by the AI & Data organization

• Ensure security, governance, and compliance standards are met across all Miral Destination AI operations

• Collaborate 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 field

• 3–5 years in ML Ops, DevOps, or ML/data engineering

• Proven experience deploying and operating ML models in production

• Strong MLOps practices and scalable AI deployment

• Hands-on experience with Databricks, MLflow, and Python

• CI/CD, containerization (Docker/Kubernetes), and infrastructure-as-code

• Model monitoring, observability, and drift detection

• Cloud platforms – AWS, Azure, or GCP

• Infrastructure optimization for cost, latency, and scalability