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Senior MLOps Engineer

Mroads — United States · Posted ~3 hours ago

Senior Onsite

Skills

AWS Amazon SageMaker MLOps ML platform operations cloud infrastructure production ML pipelines Terraform IAM MLflow model deployment model monitoring infrastructure as code AWS CDK CloudFormation Airflow AWS Step Functions Snowflake

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Summary ✨ AI‑Generated

A senior MLOps engineer is sought to build and operate production machine-learning platforms in the cloud. The role requires deep expertise in managed ML services, production pipelines, infrastructure as code, IAM, experiment tracking, workflow orchestration, model serving, monitoring, and scalable deployment. Extensive software engineering and cloud experience are expected.

Highlights

Work deeply with cloud ML infrastructure and production MLOps, owning sophisticated model training, deployment, monitoring, and rollback workflows while using leading cloud and infrastructure-as-code technologies.

Description

Mroads is looking for a "MLOps Engineer" with experience in SageMaker for one of the direct clients in Plano, TX. Requirements: 10-15 years of software engineering experience focused on cloud infrastructure or ML platform operations5+ years hands-on with AWS, including deep expertise in Amazon SageMaker (Studio, Pipelines, Model Registry, Endpoints, Feature Store)3+ years building and operating production MLOps pipelines — training, versioning, deployment, monitoring, rollbackExperience with SageMaker Unified Studio or Studio Classic — domain/project setup, blueprints, multi-tenant configurationInfrastructure-as-Code with Terraform, CDK, or CloudFormationIAM design for ML platforms — execution roles, service roles, cross-account access, Lake Formation, SSO/SAMLMLflow or equivalent experiment trackingSageMaker Pipelines or similar workflow orchestration (Airflow, Step Functions)Model serving — real-time endpoints, batch transform, auto-scaling, endpoint monitoringSnowflake as a data source for ML pipelinesKubernetes (EKS) and container orchestrationNetworking and security — VPC, security groups, private endpoints, cross-account connectivity Preferred Skills: SageMaker Unified Studio domain provisioning, custom blueprints, project standardizationSageMaker Feature Store for online/offline feature managementSageMaker Model Monitor — data quality checks, bias detection, drift detectionAWS Machine Learning Specialty certification