MLOps Engineer (SageMaker)

mroads; Plano (TX); On-site; USD 140,000 - 180,000; Full time

Job summary

Mroads is seeking an experienced MLOps Engineer in Plano, TX, focused on cloud infrastructure and ML platform operations. The role emphasizes hands-on AWS SageMaker (Studio, Pipelines, Model Registry, Endpoints, Feature Store), IaC, and scalable model serving.

You will design and operate production ML pipelines, ensure secure networking, and collaborate with data teams. Familiarity with Snowflake, Kubernetes (EKS), and monitoring will be valued.

Qualifications

• 10–15 years in software engineering focusing on cloud infra or ML platforms.
• 5+ years AWS hands-on, with SageMaker (Studio/Pipelines/Model Registry/EndPoints/Feature Store).
• 3+ years building/operating production MLOps pipelines.
• SageMaker Unified Studio or Studio Classic experience.
• Infrastructure as Code with Terraform, CDK, or CloudFormation.
• IAM design for ML platforms (roles, cross-account, Lake Formation).
• MLflow or equivalent experiment tracking.
• SageMaker Pipelines or similar orchestration (Airflow/Step Functions).
• Model serving: real-time endpoints, batch, auto-scaling, monitoring.
• Snowflake as data source for ML pipelines.
• Kubernetes (EKS) and container orchestration.
• Networking & security: VPC, SGs, private endpoints.

Skills

Cloud infrastructure

ML platform ops

Production ML pipelines

IAM design

Experiment tracking

Workflow orchestration

Model serving

Networking & security

Tools

Amazon SageMaker

Terraform

CDK

CloudFormation

Airflow

Step Functions

Kubernetes (EKS)

Snowflake

Job description

Mroads is looking for a "MLOps Engineer" with experience in SageMaker for one of the direct clients in Plano, TX.

Requirements:

• 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

Preferred Skills:

• SageMaker Feature Store for online/offline feature management
• AWS Machine Learning Specialty certification