MLOps Architect / Lead (AWS SageMaker)

Key Requirements:Hands-on experience in ML Ops, with strong focus on AWS ecosystemProven experience building end-to-end ML pipelines using AWS SageMaker, including:SageMaker PipelinesFeature StoreModel RegistryAbility to design and implement scalable, automated ML deployment workflowsCore Responsibilities:Architect and manage ML Ops infrastructure for model training, deployment, and monitoringDevelop secure, reusable, and production-grade pipelines across environmentsImplement monitoring, logging, and alerting for models and ML systemsManage complete model lifecycle: feature engineering, registry, validation, deployment, and inferenceCollaborate with data science and engineering teams to integrate ML Ops best practicesEnsure adherence to security, compliance, and data governance standardsDrive automation and continuous improvement in ML workflows and infrastructureNice to Have:Experience working in multi-environment deployments (dev/test/prod)Strong understanding of scalable architecture and cloud-native design patterns