MLOps Platform Engineer (SageMaker)

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. 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.

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 we’re 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 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

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 www.kaygen.com.

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)

Achieve your Kaizen by clicking here. A unique and exclusive talent community supported by Kaygen, that includes programs like:

• Certifications
• Mentorship Program
• Referrals
• Family and Wellness benefits
• Continuous Growth and Career Development

• Referrals
• Family and Wellness benefits
• Continuous Growth and Career Development