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Amazon Sagemaker Jobs (NOW HIRING)

Ztek Consulting INC

Data Scientist

Charlotte, NC · On-site

Design and implement end-to-end machine learning ML pipelines using services such as Amazon SageMaker AWS Glue AWS Lambda and Amazon S3 * Perform data collection cleaning and feature engineering to ...

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How much do amazon sagemaker jobs pay per year?

As of Sep 2, 2026, the average yearly pay for amazon sagemaker in the United States is $77,129.00, according to ZipRecruiter salary data. Most workers in this role earn between $58,000.00 and $96,000.00 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 the United States as of August 2026, with employment types broken down into 84% Full Time, 14% Part Time, and 2% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $77,129 per year, or $37.1 per hour.

Senior ML Platform Engineer (AWS SageMaker & MLOps)

W3Global Inc.

Plano, TX • On-site

$102.94/hr

Full-time

Re-posted 17 days ago

Job description

Our client is seeking a Senior ML Platform Engineer to lead the design, implementation, and operational management of a next-generation enterprise machine learning platform. This role will be responsible for building a scalable, secure, and governed AWS SageMaker-based MLOps ecosystem that supports the complete machine learning lifecycle-from data ingestion and model training to deployment, monitoring, and optimization.

Key Responsibilities

  • Design, build, and support an enterprise AWS SageMaker platform across multiple environments.

  • Develop and maintain production-grade MLOps pipelines for model training, validation, deployment, monitoring, and rollback.

  • Configure and manage SageMaker Studio/Unified Studio, including domains, projects, user roles, and governance controls.

  • Implement Model Registry, model versioning, lineage tracking, and promotion workflows.

  • Build and support real-time and batch model serving solutions using SageMaker endpoints.

  • Establish MLflow experiment tracking and model lifecycle management processes.

  • Partner with security and infrastructure teams to implement IAM, SSO, cross-account access, and platform governance.

  • Monitor platform performance, availability, and observability using AWS-native and third-party tools.

  • Drive automation through Infrastructure-as-Code (Terraform, CDK, or CloudFormation).

Required Qualifications

  • 10+ years of software engineering experience focused on cloud infrastructure, platform engineering, or machine learning platforms.

  • 5+ years of hands-on AWS experience, including deep expertise with Amazon SageMaker.

  • 3+ years of experience building and operating production MLOps pipelines.

  • Strong experience with SageMaker Studio Classic (Unified Studio experience highly preferred).

  • Experience with SageMaker Pipelines, Model Registry, Endpoints, and Feature Store.

  • Expertise with MLflow or comparable experiment tracking platforms.

  • Experience implementing IAM, SSO/SAML, execution roles, service roles, and cross-account access controls.

  • Strong knowledge of Snowflake integrations for machine learning workflows.

  • Experience with Kubernetes (EKS), containerized applications, and cloud-native architectures.

  • Solid understanding of AWS networking and security, including VPCs, private endpoints, security groups, and cross-account connectivity.

Preferred Qualifications

  • Experience implementing SageMaker Unified Studio environments.

  • Expertise with SageMaker Feature Store and feature management strategies.

  • Experience with SageMaker Model Monitor, drift detection, bias detection, and model performance monitoring.

  • AWS Certified Machine Learning - Specialty certification.

Why Join?

This is an opportunity to play a key role in transforming an enterprise AI/ML ecosystem by building a centralized, scalable, and governed machine learning platform that will support advanced analytics and AI initiatives across the organization.

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About W3Global

Sourced by ZipRecruiter

W3Global has been delivering staffing solutions for nearly two decades; we know which recruiting strategies work best. Our expert team is committed to developing a customized solution to fit your company’s unique needs. As a W3Global client, you’ll also receive personalized assistance from a seasoned team of staffing specialists. We are committed to providing both technical support and industry expertise to simplify the hiring process. We know that your time matters. W3Global will help you streamline the hiring process, getting it done and getting it right.

Industry

Recruiting and staffing services

Company size

501 - 1,000 Employees

Headquarters location

Frisco, TX, US

Year founded

2006

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