Studio Engineer Jobs in Texas (NOW HIRING)

Position: MLOps Platform Engineer (SageMaker)

Location: Plano, TX

Duration : 12+ Months

Job Description:

RM NOTES: 

·         Export Control form would be required but at the time of onboarding only and not required during submission.

·         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.

·         Local candidates preferred, 12 months contract with extension, Onsite role.

·         Must Haves:

o    10-15 years of software engineering experience focused on cloud infrastructure or ML platform operations

o    5+ years hands-on with AWS, including deep expertise in Amazon SageMaker (Studio Classic Studio, Pipelines, Model Registry, Endpoints, Feature Store)

o    3+ years building and operating production MLOps pipelines — training, versioning, deployment, monitoring, rollback

o    Experience with SageMaker Unified Studio or Studio Classic — domain/project setup, blueprints, multi-tenant configuration

o    MLflow or equivalent experiment tracking

o    SageMaker Pipelines or similar workflow orchestration (Airflow, Step Functions)

o    Unified Studio is preferred to have but Classic is must have.

Interview Process:

·         1st Round- MS Teams - Technical Interview – SageMaker and AWS

·         2nd Round- MS Teams - Technical Interview – SageMaker and AWS

• Client 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.
•  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

Required Skill:

• 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