MLOps Platform Engineer (SageMaker)

Ampcus Inc; Plano (TX); On-site; USD 180,000 - 210,000; Full time

Job summary

Ampcus Inc is seeking a highly motivated MLOps Platform Engineer to join the Enterprise Analytical Data & Integration Team in Plano, TX. The role focuses on designing, building, and operationalizing an enterprise ML platform on AWS SageMaker Unified Studio, with emphasis on reliable pipelines and scalable model serving.

You will own platform operations, set up SageMaker Studio, Pipelines, Model Registry, and monitoring, implement IAM and Okta/SailPoint integrations, and collaborate

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.

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.

Skills

AWS

SageMaker

MLOps pipelines

Terraform / IaC

Kubernetes / EKS

Snowflake

Python

IAM & security

CI/CD

Monitoring & observability

Tools

SageMaker Studio

SageMaker Pipelines

SageMaker Endpoints

MLflow

Terraform

CloudFormation

Airflow

Step Functions

Kubernetes

Snowflake

Okta SSO

SailPoint

Job description

Ampcus Inc. is a certified global provider of a broad range of Technology and Business consulting services. We are in search of a highly motivated candidate to join our talented Team.

Job Title:

MLOps Platform Engineer (SageMaker)

Location(s):

Onsite

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. The role involves designing, building, and operationalizing an enterprise ML platform on AWS SageMaker Unified Studio.

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. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, protected veterans or individuals with disabilities.