MLOps Platform Engineer (SageMaker)

IVIDTEK INC; Plano (TX); On-site; USD 170,000 - 210,000; Full time

Job summary

IVIDTEK INC 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 a fragmented toolchain to a unified, governed platform covering the full ML lifecycle from data discovery through model deployment and monitoring.

You will set up SageMaker Unified Studio, build MLOps pipelines, manage model registry, configure MLflow, IAM, and real-time endpoints, ensuring secure cross-account access and

Qualifications

• 10-15 years of software engineering with cloud infra or ML platform ops.
• 5+ years hands-on AWS SageMaker exposure (Studio, Pipelines, Endpoints).
• 3+ years building production MLOps pipelines for training, deployment, monitoring.

Responsibilities

• Design, build, and operate an enterprise ML platform on AWS SageMaker Unified Studio.
• Migrate tooling to a unified, governed platform with end-to-end ML lifecycle.
• Set up SageMaker Studio, multi-environment promotion workflows, and domain roles.
• Create MLOps pipelines, data ingestion from Snowflake, preprocessing, training, and evaluation.
• Manage Model Registry with cross-account promotion and lineage tracking.
• Configure MLflow tracking, IAM roles, and real-time/batch model serving.
• Ensure observability and security across the platform (CloudWatch, Datadog).

Skills

AWS SageMaker

ML Platform

Terraform

Kubernetes

IAM/Security

Tools

Snowflake

Airflow

Datadog

Okta SSO

Job description

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
• 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 Feature Store for online/offline feature management
• 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