Haystack
← Back to Jobs
Technology
RS

AWS SageMaker Engineer/ MLOps Platform Engineer

Rishabh Software Pvt. LtdPlano, TX🇺🇸United StatesPosted 3 Sept 2026

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

Seniority
Mid Senior
Work mode
On Site
Location
Plano, TX, United States
Posted
1 week ago
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.

Similar jobs

Browse related jobs

Apply now