MLOps Platform Engineer (SageMaker) (1497588)

Who we are

\tCollaborative. Respectful. A place to dream and do. These are just a few words that describe what life is like at Client. As one of the world's most admired brands, Clientis growing and leading the future of mobility through innovative, high-quality solutions designed to enhance lives and delight those we serve. We're looking for diverse, talented team members who want to Dream. Do. Grow. with us.
\tWhat we're looking for Client Enterprise Platforms team 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.
\tWhat you'll be doing - Set up SageMaker Unified Studio platform — domain configuration, project provisioning, persona-based roles, and multi-environment (Dev, Prod-UAT, Prod) promotion workflows
\tBuild MLOps pipelines using SageMaker Pipelines — data extraction from Snowflake, preprocessing, training, evaluation, and model registration
\tManage SageMaker Model Registry — cross-account model promotion, versioning, immutability, and lineage tracking
\tConfigure MLflow experiment tracking — auto-logging of parameters, metrics, and artifacts
\tSet up identity and access management — Okta SSO, SailPoint entitlements, persona-based execution roles, service roles for pipelines
\tBuild model serving — real-time SageMaker endpoints and batch prediction workflows
\tSet up model monitoring — data drift, model drift, performance degradation detection
\tConfigure data catalog — searchable datasets, access-level visibility, access-request workflows, lineage
\tOwn platform operations — observability (CloudWatch, Datadog), logging, custom images, instance availability
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\t-Requirements:
\tQualifications/ What you bring (Must Haves) – Highlight Top 3-5 skills
\t10-15 years of software engineering experience focused on cloud infrastructure or ML platform operations
\t5+ years hands-on with AWS, including deep expertise in Amazon SageMaker (Studio, Pipelines, Model Registry, Endpoints, Feature Store)
\t3+ years building and operating production MLOps pipelines — training, versioning, deployment, monitoring, rollback
\tExperience with SageMaker Unified Studio or Studio Classic — domain/project setup, blueprints, multi-tenant configuration
\tInfrastructure-as-Code with Terraform, CDK, or CloudFormation
\tIAM design for ML platforms — execution roles, service roles, cross-account access, Lake Formation, SSO/SAML
\tMLflow or equivalent experiment tracking
\tSageMaker Pipelines or similar workflow orchestration (Airflow, Step Functions)
\tModel serving — real-time endpoints, batch transform, auto-scaling, endpoint monitoring
\tSnowflake as a data source for ML pipelines
\tKubernetes (EKS) and container orchestration
\tNetworking and security — VPC, security groups, private endpoints, cross-account connectivity
\tAdded bonus if you have (Preferred): SageMaker Unified Studio domain provisioning, custom blueprints, project standardization
\tSageMaker Feature Store for online/offline feature management
\tSageMaker Model Monitor — data quality checks, bias detection, drift detection
\tAWS Machine Learning Specialty certification