Platform Engineer

B12 Consulting; Plano (TX); On-site; USD 150,000 - 190,000; Full time

Job summary

B12 Consulting in Plano, TX is seeking a Senior ML Platform Engineer to design, build, and operate 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.

You’ll design and set up SageMaker Unified Studio, build MLOps pipelines, manage the Model Registry and lineage, implement IAM and

Qualifications

• 10-15 years of software engineering focused on cloud infrastructure or ML platform ops.
• 5+ years AWS with SageMaker expertise (Studio, Pipelines, Model Registry, Endpoints, Feature Store).
• 3+ years building and operating production MLOps pipelines (training, versioning, deployment, monitoring).
• Experience with SageMaker Unified Studio or Studio Classic (domain setup, blueprints, multi-tenant).
• Infrastructure-as-Code: Terraform, CDK, or CloudFormation.
• IAM design for ML platforms — execution roles, cross-account access, SSO/SAML.
• MLflow or equivalent experiment tracking, SageMaker Pipelines orchestration.
• Model serving — real-time endpoints, batch, auto-scaling, monitoring.
• Snowflake as a data source for ML pipelines, Kubernetes (EKS).
• Networking and security — VPC, private endpoints, cross-account connectivity.

Responsibilities

• Set up SageMaker Unified Studio platform — domain config, project provisioning, roles, multi-environment promotion workflows.
• Build MLOps pipelines with SageMaker Pipelines — data extraction from Snowflake, preprocessing, training, evaluation, model registration.
• Manage SageMaker Model Registry — cross-account promotion, versioning, lineage tracking, immutability.
• Configure MLflow experiment tracking — auto-logging of parameters, metrics, artifacts.
• Set up IAM/Okta SSO and service roles for pipelines and environments.
• Build model serving — real-time endpoints and batch prediction workflows.
• Configure data catalog — searchable datasets, lineage, access requests.
• Own platform operations — observability (CloudWatch, Datadog), logging, images, instance availability.

Skills

10-15 years software engineering

SageMaker (Studio, Pipelines, Model) 5

MLOps pipelines (training, deployment,

Unified Studio / Studio Classic

Terraform/CDK/CloudFormation

IAM design for ML platforms

MLflow experiment tracking

SageMaker Pipelines orchestration

Model serving endpoints

Snowflake as data source

Kubernetes (EKS)

Networking and security (VPC, SGs)

Tools

Terraform

CDK

CloudFormation

Snowflake

Kubernetes (EKS)

Airflow / Step Functions

Job description

• 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.

What we’re looking for

Our 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

- Configure data catalog — searchable datasets, access-level visibility, access-request workflows, lineage

- Own platform operations — observability (CloudWatch, Datadog), logging, custom images, instance availability

Requirements:

• - 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

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 Feature Store for online/offline feature management
• - AWS Machine Learning Specialty certification