MLOps Platform Engineer (SageMaker)

AVA Consulting is seeking a MLOps Platform Engineer (SageMaker)

Location: Plano, TX

U.S. Citizens and those authorized to work in the U.S. are encouraged to apply. We are unable to sponsor at this time.

Company Background: Our client, a major employer in the area, is looking for a MLOps Platform Engineer (SageMaker) to be part of its team in its North American operations.

Job Description:

• Client's 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.

Responsibilities:

• 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:

Must Have 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

Preferred Skills:

• 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

NOTE: Interested Candidates can apply by sending their Updated Resume and Contact Details.

Ron Tolson

AVA Consulting

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