MLOps Platform Engineer (SageMaker)

IntelliPro Group Inc.•Plano, TX

8d•$90 - $102•Onsite

About The Position

The MLOps Platform Engineer will be responsible for setting up and managing the SageMaker Unified Studio platform, building MLOps pipelines, managing the SageMaker Model Registry, configuring MLflow experiment tracking, and setting up identity and access management. This role involves building model serving capabilities, setting up model monitoring, configuring a data catalog, and owning platform operations including observability, logging, and instance availability.

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

Nice To Haves

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

Responsibilities

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