MLOps Platform Engineer (SageMaker)

IVID TEK INC; Plano (TX); On-site; USD 123,984 - 130,872; Full time

Job summary

IVID TEK INC is seeking a Senior MLOps Platform Engineer to design, build, and operationalize an enterprise ML platform on AWS SageMaker Unified Studio. You will migrate to a unified, governed platform on AWS Landing Zone 2, covering data discovery through model deployment and monitoring.

You will set up SageMaker Unified Studio, build pipelines, manage the model registry, and enable secure, observable platform operations across multi-environment deployments.

Qualifications

• 10-15 years in software engineering focused on cloud infra or ML platform ops.
• 5+ years hands-on with AWS, incl. SageMaker (Studio, Pipelines, Model Registry, Endpoints, Feature Store).
• 3+ years building/operating production MLOps pipelines—training, versioning, deployment, monitoring.
• Experience with SageMaker Unified Studio or Studio Classic—domain/project setup, blueprints, multi-tenant config.
• IaC 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 orchestration (Airflow, Step Functions).
• Model serving—real-time endpoints, batch transform, auto-scaling, endpoint monitoring.
• Snowflake as data source for ML pipelines.
• Kubernetes (EKS) and container orchestration.
• Networking and security—VPC, security groups, private endpoints, cross-account connectivity.

Responsibilities

• Set up SageMaker Unified Studio platform — domain config, project provisioning, roles, multi-environment promotion workflows.
• Build MLOps pipelines using SageMaker Pipelines — data extraction from Snowflake, preprocessing, training, evaluation, model registration.
• Manage SageMaker Model Registry — cross-account promotion, versioning, immutability, lineage tracking.
• Configure MLflow experiment tracking — auto-logging of parameters, metrics, 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.

Skills

SageMaker

MLOps pipelines

IaC

IAM design

MLflow

SageMaker Pipelines

Snowflake

Kubernetes (EKS)

Networking & security

Observability & monitoring

SSO/SAML

Tools

Terraform

CloudFormation

AWS

Job description

Title: MLOps Platform Engineer (SageMaker)

Duration: 12 months with extension

Location: Onsite in Plano, TX 75024

Job ID – 1497588

pay - $90-95/hr W2 (USC/ GC/ H4 / EAD)

What we’re looking for:

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):

• 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

Interview Process:

1st Round- MS Teams - Technical Interview – SageMaker and AWS

2nd Round- MS Teams - Technical Interview – SageMaker and AWS

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