Machine Learning Operations (MLOps) Engineer (Only W2)

3+ years of experience building and supporting production machine learning systems. Demonstrated experience implementing end-to-end machine learning lifecycle management. Experience operating ML workloads in cloud environments (AWS, Azure, or GCP). Experience supporting both batch and real-time machine learning solutions. Experience working with large-scale structured and unstructured data.

Required Technical SkillsMachine Learning & MLOpsML lifecycle managementModel deployment and orchestrationExperiment tracking and model versioningModel monitoring and observabilityFeature Store architectureModel Registry implementationAutomated retraining workflowsResponsible AI and model governance practicesCloud PlatformsExperience with one or more:AWS SageMakerAzure Machine LearningGoogle Vertex AIProgrammingProficiency in:Python (required)SQL (required)Java, Scala, or R (preferred)Data PlatformsExperience with:SnowflakeDatabricksIBM DB2 or equivalent enterprise RDBMS platformsData lakes and modern cloud storage platformsData TransformationExperience with:dbtSparkPandasPySparkDevOps & InfrastructureExperience with:Git/GitHub/GitLabJenkins, GitHub Actions, Azure DevOps, or equivalent CI/CD toolsTerraform or infrastructure-as-code frameworksLinux administrationShell scriptingContainers & OrchestrationExperience with:DockerKubernetesOpenShift (preferred)Streaming & Real-Time TechnologiesExperience with:Apache KafkaEvent-driven architecturesReal-time inference pipelinesMonitoring & ObservabilityExperience with:PrometheusGrafanaELK StackCloud-native monitoring solutions.

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