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Amazon Sagemaker Jobs in Texas (NOW HIRING)

IVID TEK INC

MLOps Platform Engineer (SageMaker)

Plano, TX · On-site

$123.98 - $130.87/hr

... in Amazon SageMaker (Studio, Pipelines, Model Registry, Endpoints, Feature Store) * 3+ years building and operating production MLOps pipelines -- training, versioning, deployment, monitoring ...

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How much do amazon sagemaker jobs pay per year?

As of Sep 6, 2026, the average yearly pay for amazon sagemaker in Texas is $71,858.00, according to ZipRecruiter salary data. Most workers in this role earn between $54,000.00 and $89,400.00 per year, depending on experience, location, and employer.

What is Amazon SageMaker?

Amazon SageMaker is a fully managed machine learning service provided by AWS that allows developers and data scientists to build, train, and deploy machine learning models quickly and at scale. It offers a range of tools for every stage of the ML workflow, including data labeling, model training, tuning, and deployment. SageMaker supports popular ML frameworks and integrates with other AWS services, making it easier to operationalize machine learning in the cloud. Its managed infrastructure helps reduce the time and complexity involved in developing ML solutions.

What are the key skills and qualifications needed to thrive as an Amazon SageMaker machine learning engineer?

To excel as an Amazon SageMaker Machine Learning Engineer, you need strong expertise in machine learning concepts, data preprocessing, and programming languages such as Python, along with a degree in computer science or a related field. Familiarity with AWS SageMaker, cloud infrastructure, version control systems like Git, and relevant certifications such as AWS Certified Machine Learning – Specialty are highly beneficial. Exceptional problem-solving, communication, and collaboration skills help you work effectively with cross-functional teams and stakeholders. These skills are vital for building, deploying, and maintaining scalable machine learning solutions that drive business value.

What are some common challenges faced by professionals working with Amazon SageMaker, and how can they be addressed?

Professionals working with Amazon SageMaker often encounter challenges such as managing large datasets, optimizing model training costs, and integrating SageMaker with other AWS services or existing data pipelines. Addressing these challenges typically involves leveraging SageMaker's built-in data preprocessing features, using managed spot training to reduce costs, and collaborating closely with data engineering and DevOps teams to ensure seamless integration. Regularly reviewing AWS documentation and best practices can also help professionals stay updated on new features and solutions.

What can I do with Amazon Sagemaker?

Amazon Sagemaker is a cloud-based machine learning platform that allows data scientists and developers to build, train, and deploy machine learning models efficiently. It provides tools for data labeling, model tuning, and deployment, supporting various frameworks like TensorFlow and PyTorch. Users can automate workflows, manage models at scale, and integrate with other AWS services for end-to-end machine learning solutions.

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Amazon Sagemaker jobs near you
Infographic showing various Amazon Sagemaker job openings in Texas as of August 2026, with employment types broken down into 91% Full Time, 6% Part Time, and 3% Contract. Highlights an 84% Physical, 6% Hybrid, and 10% Remote job distribution, with an average salary of $71,858 per year, or $34.5 per hour.

AWS SageMaker Engineer/ MLOps Platform Engineer

Rishabh Software Pvt. Ltd

Plano, TX • On-site

Other

Posted 3 days ago

New

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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