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Aws Sagemaker Delete Training Jobs (NOW HIRING)

Amazon

Senior SDE, AWS SageMaker Data Preparation

Bellevue, WA · Remote

$138K - $182K/yr

... SageMaker AI. Are you interested to play a key role in developing software applications at AWS. We ... training, fine-tuning, and evaluation without needing deep data engineering expertise. As a Senior ...

Amazon

Senior SDE, AWS SageMaker Data Preparation

Bellevue, WA · Remote

$138K - $182K/yr

... SageMaker AI. Are you interested to play a key role in developing software applications at AWS. We ... training, fine-tuning, and evaluation without needing deep data engineering expertise. As a Senior ...

Amazon

Senior SDE, AWS SageMaker Data Preparation

Bellevue, WA · Remote

$138K - $182K/yr

... SageMaker AI. Are you interested to play a key role in developing software applications at AWS. We ... training, fine-tuning, and evaluation without needing deep data engineering expertise. As a Senior ...

Amazon

Senior SDE, AWS SageMaker Data Preparation

Bellevue, WA · On-site

$138K - $182K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... SageMaker AI? Are you interested to play a key role in developing software applications at AWS? We ... training, fine-tuning, and evaluation without needing deep data engineering expertise. As a Senior ...

IVID TEK INC

MLOps Platform Engineer (SageMaker)

Plano, TX · On-site

$123.98 - $130.87/hr

... with AWS, including deep expertise in Amazon SageMaker (Studio, Pipelines, Model Registry, Endpoints, Feature Store) * 3+ years building and operating production MLOps pipelines -- training ...

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Aws Sagemaker Delete Training information

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How much do aws sagemaker delete training jobs pay per hour?

As of Aug 19, 2026, the average hourly pay for aws sagemaker delete training in the United States is $50.23, according to ZipRecruiter salary data. Most workers in this role earn between $23.56 and $74.04 per hour, depending on experience, location, and employer.

What does it mean to delete a training job in AWS SageMaker?

Deleting a training job in AWS SageMaker means removing the record of the training job from your SageMaker environment. This action does not delete the underlying resources such as the S3 bucket containing input/output data, or the model artifacts generated during training. It simply removes the job entry from the SageMaker console and API listings. Deleting a training job can help keep your environment organized, but you should ensure that all necessary artifacts are saved before deletion since the job's logs and metadata will no longer be accessible.

What are the typical responsibilities and challenges involved in managing and deleting training jobs in AWS SageMaker?

As someone managing AWS SageMaker training jobs, you'll often be responsible for monitoring job statuses, ensuring resources are used efficiently, and deleting completed or obsolete training jobs to optimize costs and maintain a tidy environment. A common challenge is making sure all associated resources, such as model artifacts and logs, are properly cleaned up to avoid unnecessary charges. Collaboration with data scientists and ML engineers is typical, as you'll coordinate on when it's safe to remove jobs without disrupting ongoing projects or model evaluations.

What are the key skills and qualifications needed to thrive as an AWS SageMaker machine learning engineer, and why are they important?

To thrive as an AWS SageMaker Machine Learning Engineer, you need strong proficiency in machine learning, data science, and cloud computing, with an educational background in computer science or a related field. Experience with AWS SageMaker, Python programming, and familiarity with CI/CD pipelines and cloud security is typically required, along with AWS certifications like AWS Certified Machine Learning – Specialty. Critical thinking, problem-solving, and effective communication are standout soft skills for this role. These skills are essential to successfully develop, deploy, and manage scalable machine learning models while collaborating with cross-functional teams in a cloud environment.

What is the difference between Aws Sagemaker Delete Training vs Data Scientist?

AspectAws Sagemaker Delete TrainingData Scientist
Primary RoleManaging and deleting training jobs in AWS SagemakerAnalyzing data, building models, and deriving insights
Required SkillsAWS services, Sagemaker platform, scriptingStatistics, programming (Python/R), data analysis
Work EnvironmentCloud-based, AWS platformVaries: office, remote, or cloud-based
CertificationsAWS certifications helpfulData science or analytics certifications beneficial

While Aws Sagemaker Delete Training focuses on managing specific training jobs within AWS, Data Scientists work broadly on data analysis and model development. Both roles require technical skills but differ in scope and responsibilities.

More about Aws Sagemaker Delete Training jobs

What job categories do people searching Aws Sagemaker Delete Training jobs look for?

The top searched job categories for Aws Sagemaker Delete Training jobs are:

Aws Sagemaker Delete Training jobs near you
Infographic showing various Aws Sagemaker Delete Training job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 16% Part Time, and 1% Contract. Highlights an 89% Physical, 1% Hybrid, and 10% Remote job distribution, with an average salary of $104,480 per year, or $50.2 per hour.

Senior ML Platform Engineer (AWS SageMaker & MLOps)

W3Global Inc.

Plano, TX • On-site

$102.94/hr

Full-time

Re-posted 3 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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