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AWS SageMaker Engineer/ MLOps Platform Engineer

Rishabh Software Pvt. LtdPlano, TX🇺🇸United States

Why this role stands out

This role offers a fantastic opportunity to deepen your expertise in AWS SageMaker and MLOps within a reputable company, contributing to impactful projects. You'll thrive here if you have extensive experience in cloud infrastructure and a passion for building robust ML pipelines, making it an ideal next step for your career growth.

Quick overview

SeniorityMid Senior
Work modeOn Site
LocationPlano, TX, United States
AWSMLOpsMLflowMachine LearningSAMLSSOSnowflakeAirflowCDKCloudFormationDatadogKubernetesTerraform

Job description

Job Title: MLOps Platform Engineer (SageMaker)
Location: Plano, TX (Onsite)
Duration: 12 Months
Description:
RM Notes:
  • Export Control form will be required during onboarding only and is not required at the time of submission.
  • This position is with the Enterprise Analytical Data & Integration Team.
  • The hiring manager is looking to onboard an experienced MLOps Platform Engineer with strong expertise in AWS and Amazon SageMaker.
  • Local candidates are preferred.
  • 12-month contract with possible extension.
  • Onsite role.
Must-Have Skills
  • 10 15 years of software engineering experience focused on cloud infrastructure or ML platform operations.
  • 5+ years of hands-on AWS experience, including deep expertise in Amazon SageMaker (Studio Classic/Studio, Pipelines, Model Registry, Endpoints, Feature Store).
  • 3+ years of experience building and operating production MLOps pipelines, including training, versioning, deployment, monitoring, and rollback.
  • Experience with SageMaker Unified Studio or Studio Classic, including domain/project setup, blueprints, and multi-tenant configuration.
  • MLflow or equivalent experiment tracking tools.
  • SageMaker Pipelines or similar workflow orchestration tools (Airflow, Step Functions).
  • SageMaker Unified Studio experience is preferred; Studio Classic experience is mandatory.
What We're Looking For
Client is seeking a Senior ML Platform Engineer to design, build, and operationalize an enterprise ML platform on AWS SageMaker Unified Studio.
The selected candidate will help migrate the organization from a fragmented ML toolchain to a unified, governed platform on AWS Landing Zone 2, supporting the complete machine learning lifecycle-from data discovery through model deployment and monitoring.
Key Responsibilities
  • Set up SageMaker Unified Studio platform, including domain configuration, project provisioning, persona-based roles, and multi-environment (Dev, Prod-UAT, Prod) promotion workflows.
  • Build MLOps pipelines using SageMaker Pipelines for data extraction from Snowflake, preprocessing, training, evaluation, and model registration.
  • Manage SageMaker Model Registry, including cross-account model promotion, versioning, immutability, and lineage tracking.
  • Configure MLflow experiment tracking with auto-logging of parameters, metrics, and artifacts.
  • Set up identity and access management, including Okta SSO, SailPoint entitlements, persona-based execution roles, and service roles for pipelines.
  • Build model serving solutions using real-time SageMaker endpoints and batch prediction workflows.
  • Implement model monitoring for data drift, model drift, and performance degradation detection.
  • Configure data catalog capabilities, including searchable datasets, access-level visibility, access-request workflows, and lineage tracking.
  • Own platform operations, including observability (CloudWatch, Datadog), logging, custom images, and instance availability management.
Required Qualifications
Qualifications / What You Bring (Must-Haves)
  • 10 15 years of software engineering experience focused on cloud infrastructure or ML platform operations.
  • 5+ years of hands-on AWS experience with strong expertise in:
    • Amazon SageMaker Studio
    • SageMaker Pipelines
    • Model Registry
    • Endpoints
    • Feature Store
  • 3+ years of experience building and operating production MLOps pipelines, including training, versioning, deployment, monitoring, and rollback.
  • Experience with SageMaker Unified Studio or Studio Classic, including domain/project setup, blueprints, and multi-tenant configurations.
  • Infrastructure-as-Code experience using Terraform, CDK, or CloudFormation.
  • IAM design for ML platforms, including execution roles, service roles, cross-account access, Lake Formation, and SSO/SAML.
  • MLflow or equivalent experiment tracking platform experience.
  • SageMaker Pipelines or similar orchestration frameworks (Airflow, Step Functions).
  • Experience with model serving, including real-time endpoints, batch transform, auto-scaling, and endpoint monitoring.
  • Experience using Snowflake as a data source for ML pipelines.
  • Kubernetes (EKS) and container orchestration experience.
  • Strong understanding of networking and security concepts, including VPCs, security groups, private endpoints, and cross-account connectivity.
Preferred Qualifications
  • SageMaker Unified Studio domain provisioning, custom blueprints, and project standardization.
  • SageMaker Feature Store for online/offline feature management.
  • SageMaker Model Monitor, including data quality checks, bias detection, and drift detection.
  • AWS Machine Learning Specialty Certification.

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