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Amazon Sagemaker Jobs (NOW HIRING)

Accenture Federal Services

Data Engineer

Suitland, MD

$123K - $148K/yr

Support the implementation, deployment, and scaling of machine learning models in production environments using tools like Amazon SageMaker, MLflow, or Kubeflow. * Monitoring & Troubleshooting

Accenture Federal Services

Data Engineer

Suitland, MD · On-site

$123K - $148K/yr

... tools like Amazon SageMaker, MLflow, or Kubeflow. • Monitor data pipeline health, troubleshoot issues, and ensure data consistency using tools such as Amazon CloudWatch, Datadog, or Great ...

Cargill

Senior Data Scientist

Wayzata, MN

$148K/yr

Amazon SageMaker and AWS; time series modeling and statistical libraries including sktime, prophet, ARIMA, ETS, Croston, ThetaForecaster, AutoETS, AutoARIMA, and ExponentialSmoothing; machine ...

SID Global Solutions

Technical Program Manager

Exton, PA · On-site

$124K - $161K/yr

... Amazon SageMaker are productionready and aligned to delivery timelines Edge & Industrial Integration Drive programs that integrate vision outputs into: Dashboards and operational tools APIs and ...

Cargill

Senior Data Scientist

Wayzata, MN

$148K/yr

Amazon SageMaker and AWS; time series modeling and statistical libraries including sktime, prophet, ARIMA, ETS, Croston, ThetaForecaster, AutoETS, AutoARIMA, and ExponentialSmoothing; machine ...

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All JobsAmazon Sagemaker Jobs

Amazon Sagemaker information

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$23K

$77.1K

$122K

How much do amazon sagemaker jobs pay per year?

As of Jul 22, 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 are Amazon SageMaker training jobs?

Amazon SageMaker training jobs are processes that train machine learning models using specified datasets and algorithms within the SageMaker environment. They involve configuring training parameters, selecting instance types, and monitoring progress through the SageMaker console or APIs. These jobs enable scalable, managed training for developing accurate models efficiently.

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 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 is the purpose of Amazon SageMaker processing jobs?

Amazon SageMaker processing jobs are used by data scientists and machine learning engineers to perform data preprocessing, feature engineering, model evaluation, and inference tasks at scale. These jobs enable efficient data handling and model validation within the SageMaker environment, supporting the development and deployment of machine learning models.

Are AWS jobs still in demand?

Amazon SageMaker jobs and other AWS roles remain in demand due to the growing adoption of cloud computing and machine learning. Skills in cloud services, data analysis, and AI tools are highly sought after, with many organizations expanding their cloud infrastructure and AI capabilities.

What are the key skills and qualifications needed to thrive as an Amazon SageMaker Machine Learning Engineer, and why are they important?

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 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 at scale. It provides tools for data labeling, model tuning, and deployment, enabling efficient development of AI solutions. Users can also utilize built-in algorithms and integrate with other AWS services for comprehensive machine learning workflows.
More about Amazon Sagemaker jobs
What states have the most Amazon Sagemaker jobs? States with the most job openings for Amazon Sagemaker jobs include:
What job categories do people searching Amazon Sagemaker jobs look for? The top searched job categories for Amazon Sagemaker jobs are:
Amazon Sagemaker jobs near you
Infographic showing various Amazon Sagemaker job openings in the United States as of July 2026, with employment types broken down into 50% Full Time, 8% Temporary, and 42% Contract. Highlights an 75% In-person, and 25% Remote job distribution, with an average salary of $77,129 per year, or $37.1 per hour.
AI Engineer in Reston VA

AI Engineer in Reston VA

Hexaware Technologies, Inc

Reston, VA • On-site

Other

Posted 27 days ago

Job description

AI Engineer

Reston VA

Preferred qualifications:

  • Design and develop Generative AI applications using Large Language Models.
    Build Retrieval-Augmented Generation (RAG) solutions using vector databases.
    Design cloud-native AI solutions using Amazon Bedrock / Amazon SageMaker
    Develop autonomous AI agents using: LangChain, LangGraph, Amazon Bedrock Agents
    Create multi-agent workflows and orchestration frameworks.
    Integrate agents with enterprise applications and APIs.
    Build human-in-the-loop workflows for governance and validation.
    Develop prompt engineering frameworks and AI agents.
    Fine-tune and customize foundation models based on business requirements.
    Implement guardrails, governance, and responsible AI practices.
    Deploy AI models using CI/CD pipelines and MLOps frameworks.
    Ensure compliance with AI governance, data privacy, and security standards.

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