MLOps Platform Engineer (SageMaker)
Plano, Texas, United States of America
Contract
1 week ago
Key skills
Machine LearningMLMLOpsMLflowSnowflakeAirflowAWSTerraformKubernetesEKSCloudFormationIAMCloudWatchSageMakerOktaSAMLSSODatadog
About this role
Location: Plano, TX Salary: $85.00 USD Hourly - $95.00 USD Hourly Description:
Job Title: MLOps Platform Engineer (SageMaker)
Location: Plano, TX
Contract
Must Haves:
What 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
- 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
Requirements:
Qualifications/ What you bring (Must Haves) - Highlight Top 3-5 skills
- 10-15 years of software engineering experience focused on cloud infrastructure or ML platform operations
- 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, 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 you 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
By providing your phone number, you consent to: (1) receive automated text messages and calls from the Judge Group, Inc. and its affiliates (collectively "Judge") to such phone number regarding job opportunities, your job application, and for other related purposes. Message & data rates apply and message frequency may vary. Consistent with Judge's Privacy Policy, information obtained from your consent will not be shared with third parties for marketing/promotional purposes. Reply STOP to opt out of receiving telephone calls and text messages from Judge and HELP for help.
Contact:
This job and many more are available through The Judge Group. Please apply with us today!
Job Title: MLOps Platform Engineer (SageMaker)
Location: Plano, TX
Contract
Must Haves:
- 10-15 years of software engineering experience focused on cloud infrastructure or ML platform operations.
- 5+ years hands-on with AWS, including deep expertise in Amazon SageMaker (Studio Classic Studio, Pipelines, Model Registry, Endpoints, Feature Store)
- 3+ 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
- MLflow or equivalent experiment tracking
- SageMaker Pipelines or similar workflow orchestration (Airflow, Step Functions)
- Unified Studio is preferred to have but Classic is must have.
What 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
- 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
Requirements:
Qualifications/ What you bring (Must Haves) - Highlight Top 3-5 skills
- 10-15 years of software engineering experience focused on cloud infrastructure or ML platform operations
- 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, 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 you 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
By providing your phone number, you consent to: (1) receive automated text messages and calls from the Judge Group, Inc. and its affiliates (collectively "Judge") to such phone number regarding job opportunities, your job application, and for other related purposes. Message & data rates apply and message frequency may vary. Consistent with Judge's Privacy Policy, information obtained from your consent will not be shared with third parties for marketing/promotional purposes. Reply STOP to opt out of receiving telephone calls and text messages from Judge and HELP for help.
Contact:
This job and many more are available through The Judge Group. Please apply with us today!