Sagemaker Engineer | Must work on W2
Plano, Illinois, United States of America
Contract
2 weeks ago
$70 - $80 USD
Key skills
Machine LearningMLMLOpsMLflowSnowflakeAirflowAWSTerraformKubernetesEKSCloudFormationIAMCloudWatchSageMakerOktaSAMLSSODatadog
About this role
Role: Sagemaker Engineer
Location: Onsite in Plano, TX
Client is 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.
- Candidates 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.
Candidates will be doing:
- Set up SageMaker Unified Studio platform domain configuration, project provisioning, persona-based roles, and multi-environment (Dev, Prod-UAT, Prod) promotion workflows
- Build MLOps pipelines using SageMaker Pipelines data extraction from Snowflake, preprocessing, training, evaluation, and model registration
- Manage SageMaker Model Registry cross-account model promotion, versioning, immutability, and lineage tracking
- Configure MLflow experiment tracking auto-logging of parameters, metrics, and artifacts
- Set up identity and access management Okta SSO, SailPoint entitlements, persona-based execution roles, service roles for pipelines
- Build model serving real-time SageMaker endpoints and batch prediction workflows
- Set up model monitoring data drift, model drift, performance degradation detection
- Configure data catalog searchable datasets, access-level visibility, access-request workflows, lineage
- Own platform operations observability (CloudWatch, Datadog), logging, custom images, instance availability
Qualifications/ What candidates bring (Must Haves) Highlight Top 3 to 5 skills:
- 10 to 15 years of software engineering experience focused on cloud infrastructure or ML platform operations
- 5 plus years hands-on with AWS, including deep expertise in Amazon SageMaker (Studio, Pipelines, Model Registry, Endpoints, Feature Store)
- 3 plus years building and operating production MLOps pipelines training, versioning, deployment, monitoring, rollback
- Experience with SageMaker Unified Studio or Studio Classic domain/project setup, blueprints, multi-tenant configuration
- Infrastructure-as-Code with Terraform, CDK, or CloudFormation
- IAM design for ML platforms execution roles, service roles, cross-account access, Lake Formation, SSO/SAML
- MLflow or equivalent experiment tracking
- SageMaker Pipelines or similar workflow orchestration (Airflow, Step Functions)
- Model serving real-time endpoints, batch transform, auto-scaling, endpoint monitoring
- Snowflake as a data source for ML pipelines
- Kubernetes (EKS) and container orchestration
- Networking and security VPC, security groups, private endpoints, cross-account connectivity
Added bonus if candidates have (Preferred):
- SageMaker Unified Studio domain provisioning, custom blueprints, project standardization
- SageMaker Feature Store for online/offline feature management
- SageMaker Model Monitor data quality checks, bias detection, drift detection
- AWS Machine Learning Specialty certification
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Asher Williams
Desk: 2o1.497.1o1o X:1o5 | Direct: 551.272.o129
asher (at) pullskill dot com