MLOps Platform Engineer (SageMaker)

Job Summary

This position is with the Enterprise Analytical Data & Integration Team. The ideal candidate will have extensive experience in cloud infrastructure or ML platform operations, with a specific focus on AWS and Amazon SageMaker.

Key Responsibilities

• Set up SageMaker Unified Studio platform — domain configuration, project provisioning, persona-based roles, and multi-environment 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 Qualifications

• 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.

Ampcus is an Equal Opportunity Employer.