MLOps Platform Engineer (SageMaker)

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. 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.

Job Title: MLOps Platform Engineer (SageMaker)
Location: Plano TX Onsite
Duration: 12-month contract with possible extension
 
Interview Process:

• 1st RoundMS Teams Technical Interview – SageMaker and AWS
• 2nd RoundMS Teams Technical Interview – SageMaker and AWS

 
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 we’re looking for
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.
 
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

Why TalentBurst?
At TalentBurst, we deliver more than talent, we deliver outcomes. We partner with you to move quickly and connect you to opportunities aligned with your skills and long term growth.

Backed by precision, transparency, and results, we connect top talent with leading organizations through trusted partnerships.

We offer competitive compensation and comprehensive benefits, including medical, dental, vision, and retirement options.

TalentBurst is an equal opportunity employer committed to an inclusive and diverse workforce.