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Aws Sagemaker Jobs (NOW HIRING)

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Python Developer - Gen AI, Sagemaker, AWS

Reston, VA · On-site

$52.25 - $72/hr

SQL * AWS Data Services * LLM * ML * Sagemaker Experience: 8+ years overall in Software Engineering disciplines, preferably in the financial services industry 2-3 years of experience in AI/ML ...

Judge Group, Inc.

AWS Cloud Engineer

Minneapolis, MN · On-site

$110K - $120K/yr

Design and develop machine learning models using AWS SageMaker, integrated with Jupyter Notebook, to enable scalable model training, evaluation, and deployment. Design solutions for large data ...

Perficient

Lead SageMaker Platform Engineer

Saint Louis, MO · On-site

$96K - $126K/yr

Rapidly triage failures using AWS logs and telemetry (CloudWatch, CloudTrail, SageMaker pipeline/execution logs, etc.) and pinpoint root causes. * Untangle permissions issues across pipeline ...

Aivra Health LLC

AI/ML Engineer

California City, CA · On-site

AWS SageMaker / Azure ML / Google Cloud Platform Vertex AI * Git Preferred Qualifications * Bachelor''s or Master''s degree in Computer Science, AI, Data Science, or related field. * Experience with ...

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How much do aws sagemaker jobs pay per hour?

As of Jul 25, 2026, the average hourly pay for aws sagemaker in the United States is $54.05, according to ZipRecruiter salary data. Most workers in this role earn between $38.70 and $64.42 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Aws Sagemaker position, and why are they important?

To excel in an AWS SageMaker-focused role, you need strong expertise in machine learning, data science, and cloud computing, often supported by a degree in computer science or a related field. Experience with AWS SageMaker, other AWS services (like S3 and Lambda), and certifications such as AWS Certified Machine Learning – Specialty are highly valued. Strong analytical thinking, problem-solving abilities, and effective communication skills help set candidates apart. Mastery of these areas ensures successful design, deployment, and management of scalable AI/ML solutions in dynamic business environments.

What are the main day-to-day responsibilities for someone working with AWS SageMaker?

Day-to-day responsibilities for AWS SageMaker professionals typically include building, training, and deploying machine learning models using the SageMaker platform, preprocessing data, and monitoring the performance of deployed models. You'll often collaborate with data engineers, software developers, and business stakeholders to understand project requirements and deliver solutions that meet business objectives. Routine tasks may also involve tuning model hyperparameters, optimizing resource usage, and ensuring data security and compliance within AWS environments. This hands-on, collaborative workflow provides the opportunity to directly impact business outcomes while continuously developing your technical expertise.

What is an AWS SageMaker job?

An AWS SageMaker job typically refers to a role focused on building, training, and deploying machine learning models using Amazon SageMaker. Professionals in this role work with data preprocessing, model optimization, and cloud-based machine learning workflows. Responsibilities may include automating ML pipelines, monitoring performance, and integrating SageMaker with other AWS services. Knowledge of Python, TensorFlow, PyTorch, and AWS services is often required.

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Aws Sagemaker jobs near you
Infographic showing various Aws Sagemaker job openings in the United States as of July 2026, with employment types broken down into 94% Full Time, 1% Part Time, and 5% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution, with an average salary of $112,422 per year, or $54 per hour.
Senior ML Platform Engineer (AWS SageMaker & MLOps)

Senior ML Platform Engineer (AWS SageMaker & MLOps)

W3Global Inc.

Plano, TX

$102.94/hr

Full-time

Posted 7 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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