MLOps Platform Engineer (SageMaker)

KAYGEN is an emerging leader in providing top talent for technology-based staffing services. We specialize in providing high-volume contingent staffing direct hire staffing and project-based solutions to companies worldwide ranging from startups to Fortune 500 and Managed Service Providers (MSP) across a wide variety of industries.

KAYGEN is an emerging leader in providing top talent for technology based staffing services. We specialize in providing high-volume contingent staffing direct hire staffing and project based solutions to companies worldwide ranging from startups to Fortune 500 and Managed Service Providers (MSP) across a wide variety of industries.

Job Description:
Must Haves:

• 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 Classic 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
• MLflow or equivalent experiment tracking
• SageMaker Pipelines or similar workflow orchestration (Airflow Step Functions)
• Unified Studio is preferred to have but Classic is must have.

Role:

• What were looking for Our Client Enterprise Platforms team is looking for a Senior ML Platform Engineer to design build and operationalize an enterprise ML platform on AWS SageMaker Unified Studio.
• You will migrate the organization from a fragmented ML toolchain to a unified governed platform on AWS Landing Zone 2 covering the full ML lifecycle from data discovery through model deployment and monitoring.

What youll 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

At KAYGEN we are always looking for dynamic talented and experienced individuals. We invite you to join our team of talented IT professionals consulting at client locations across the globe. Our culture is team-orientated; we strive to stand by our core values of respect honesty and integrity. Our team of experienced staffing experts will work with you to find you the best opportunity. For more information please visit us at .

Benefits with Kaygen
-Healthcare Insurance
-Vision and Dental Insurance
-401(k) Retirement Plan
-Free Life Insurance
-Vacation Time Off
-Sick Time Off
-Family Medical Leave (FMLA)

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