MLOps Platform Engineer (SageMaker)
Job DescriptionJob Title: MLOps Platform Engineer (SageMaker)Job Location: Plano, TXProject Duration: 12 months with possible extensionJob SummaryWhat we're looking forClient is looking for a Senior ML Platform Engineer to design, build, and operationalize an enterprise ML platform on AWS SageMaker Unified Studio. You will migrate the organization from a fragmented ML toolchain to a unified, governed platform on AWS Landing Zone 2, covering the full ML lifecycle from data discovery through model deployment and monitoring.What you'll be doingSet up SageMaker Unified Studio platform -domain configuration, project provisioning, persona-based roles, and multi-environment (Dev, Prod-UAT, Prod) promotion workflowsBuild MLOps pipelines using SageMaker Pipelines -data extraction from Snowflake, preprocessing, training, evaluation, and model registrationManage SageMaker Model Registry -cross-account model promotion, versioning, immutability, and lineage trackingConfigure MLflow experiment tracking -auto-logging of parameters, metrics, and artifactsSet up identity and access management -Okta SSO, SailPoint entitlements, persona-based execution roles, service roles for pipelinesBuild model serving -real-time SageMaker endpoints and batch prediction workflowsSet up model monitoring -data drift, model drift, performance degradation detectionConfigure data catalog -searchable datasets, access-level visibility, access-request workflows, lineageOwn platform operations -observability (CloudWatch, Datadog), logging, custom images, instance availabilityRequirements-Qualifications/ What you bring (Must Haves) -Highlight Top 3-5 skills10-15 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 configurationUnified Studio is preferred to have but Classic is must have.Infrastructure-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 connectivityAdded bonus if you have (Preferred):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 certificationMeet Your RecruiterPeter Jackson