Senior MLOps Engineer – AWSExperience: 5–7 YearsLocation: Cairo, Egypt (Onsite Role)Employment Type: Full-TimeJob SummaryWe are looking for a skilled and hands-on Senior MLOps Engineer with strong AWS expertise to support the deployment, automation, and monitoring of machine learning models in production. The ideal candidate will collaborate closely with Data Science and Engineering teams to operationalize ML models using cloud-native best practices.Key ResponsibilitiesDesign and implement end-to-end MLOps pipelines from data ingestion to model deploymentDeploy and manage ML models using AWS-native services such as SageMakerBuild and maintain CI/CD pipelines for ML workflowsImplement model monitoring, performance tracking, and basic drift detectionContainerize ML workloads using Docker and deploy on EKS/ECSSupport infrastructure automation using Terraform or CloudFormationEnsure scalability, availability, and security of ML systemsCollaborate with cross-functional teams to productionize ML solutionsTroubleshoot ML pipelines and cloud infrastructure issuesRequired Skills & QualificationsMLOps & Machine Learning5–7 years of overall experience with at least 3+ years in MLOps or ML production environmentsExperience managing ML lifecycle (training, deployment, monitoring)Hands-on experience with TensorFlow, PyTorch, or Scikit-learnExperience with MLflow or similar experiment tracking toolsAWS Cloud (Mandatory)Hands-on experience with:Amazon SageMakerS3, EC2, LambdaIAM, CloudWatchECR, ECS or EKSUnderstanding of secure and scalable AWS architectureDevOps & AutomationDocker and containerizationCI/CD using GitHub Actions, GitLab CI, Jenkins, or AWS CodePipelineInfrastructure as Code (Terraform or CloudFormation)Programming & DataStrong Python programming skillsExperience with SQL and working knowledge of NoSQL databasesExperience handling structured and unstructured datasetsGood to HaveExposure to feature stores and data versioningAWS Associate-level certificationBasic understanding of ML governance and compliance