Machine Learning Engineer

Job Title: MLOps Platform Engineer (SageMaker)Job Location: Plano, TX (100% Onsite) - LOCAL OnlyProject Duration: 12+ months with possible extension(W2 Position only- NO C2C)UPDATE- MLOPS with at least 2+ years experience in AWS Sagemaker Studio Classic OR AWS SageMaker Unified Studio is mandatory hereJob 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.This position is with Enterprise Analytical Data & Integration Team and the hiring manager is looking to onboard MLOpsPlatform Engineer (Sagemaker) who is expert in Sagemaker (key skillset) and AWS.Roles:Set 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 availabilityNOTE- Please ensure the candidate’s education details (degree, university, and graduation year) are included in the resume for reviewRequirements:5+ 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.SageMaker Studio/Unified Studio, Pipelines, Model Registry, EndpointsMLflow, Terraform/CDK/CloudFormationIAM, Snowflake, EKS/Kubernetes, AWS Networking & Security