Job description

Job Title: MLOps Platform Engineer (SageMaker) Duration: 12 Months Location: Plano, TX Pay Rate: $90/hr - $102/hr on W2 What you’ll be doing • 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 Requirements: Qualifications/ What you bring (Must Haves) – Highlight Top 3-5 skills • 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 About Us: Founded in 2009, IntelliPro is a global leader in talent acquisition and HR solutions. Our commitment to delivering unparalleled service to clients, fostering employee growth, and building enduring partnerships sets us apart. We continue leading global talent solutions with a dynamic presence in over 160 countries, including the USA, China, Canada, Singapore, Japan, Philippines, UK, India, Netherlands, and the EU. IntelliPro, a global leader connecting individuals with rewarding employment opportunities, is dedicated to understanding your career aspirations. As an Equal Opportunity Employer, IntelliPro values diversity and does not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, genetic information, disability, or any other legally protected group status. Moreover, our Inclusivity Commitment emphasizes embracing candidates of all abilities and ensures that our hiring and interview processes accommodate the needs of all applicants. Learn more about our commitment to diversity and inclusivity at intelliprogroup.com . Compensation: The pay offered to a successful candidate will be determined by various factors, including education, work experience, location, job responsibilities, certifications, and more. Additionally, IntelliPro provides a comprehensive benefits package, all subject to eligibility.

Requirements

Must-haves

Job Title: MLOps Platform Engineer (SageMaker)
Duration: 12 Months
Location: Plano, TX
Pay Rate: $90/hr - $102/hr on W2
What you’ll be doing • 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
Qualifications/ What you bring (Must Haves) – Highlight Top 3-5 skills • 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

Nice-to-haves

Added bonus if you have (Preferred): • SageMaker Unified Studio domain provisioning, custom blueprints, project standardization

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Keywords to mirror

mlawskubernetesmachine learning

Platform: jazzhr
Effort: medium

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