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Amazon Sagemaker Jobs (NOW HIRING)

Sahi Softtech

ML Ops Engineer

Phoenix, AZ · On-site

Deploy, manage, and monitor machine learning models on AWS using services such as Amazon SageMaker, AWS Lambda, Amazon ECS, Amazon EKS, and API Gateway. Build scalable model serving solutions for ...

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 ...

Ascentt

Senior Machine Learning Engineer

Plano, TX · On-site

$100K - $137K/yr

Utilize Amazon SageMaker or similar platforms for building, training, and deploying models in a production-grade environment. * Collaborate closely with data engineers, data scientists, and product ...

Ascentt

Senior Machine Learning Engineer

Plano, TX · On-site

$100K - $137K/yr

Utilize Amazon SageMaker or similar platforms for building, training, and deploying models in a production-grade environment. * Collaborate closely with data engineers, data scientists, and product ...

E-Solutions, Inc.

AWS 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 ...

B12 Consulting

MLOps

Plano, TX · On-site

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

Amazon

Manager, Analytics (ADBL191)

Newark, NJ · On-site

$109K - $185K/yr

... Amazon SageMaker Salary: $109,500 - $185,000 / year. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation ...

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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.
MLOps Platform Engineer (SageMaker)

MLOps Platform Engineer (SageMaker)

IntelliPro Group Inc.

Plano, TX • On-site

Contractor

Re-posted 3 days ago

Job description

Job Title: MLOps Platform Engineer (SageMaker)
Duration: 12 Months
Location: Plano, TX
Pay Rate: $90/hr - $102/hr on W2
What you’ll be doing     
  • Set up SageMaker Unified Studio platform — domain configuration, project provisioning, persona-based roles, and multi-environment (Dev, Prod-UAT, Prod) promotion workflows
  • Build MLOps pipelines using SageMaker Pipelines — data extraction from Snowflake, preprocessing, training, evaluation, and model registration
  • Manage SageMaker Model Registry — cross-account model promotion, versioning, immutability, and lineage tracking
  • Configure MLflow experiment tracking — auto-logging of parameters, metrics, and artifacts
  • Set up identity and access management — Okta SSO, SailPoint entitlements, persona-based execution roles, service roles for pipelines
  • Build model serving — real-time SageMaker endpoints and batch prediction workflows
  • Set up model monitoring — data drift, model drift, performance degradation detection
  • Configure data catalog — searchable datasets, access-level visibility, access-request workflows, lineage
  • Own platform operations — observability (CloudWatch, Datadog), logging, custom images, instance availability
 Requirements:
Qualifications/ What you bring (Must Haves) – Highlight Top 3-5 skills    
  • 10-15 years of software engineering experience focused on cloud infrastructure or ML platform operations
  • 5+ years hands-on with AWS, including deep expertise in Amazon SageMaker (Studio, Pipelines, Model Registry, Endpoints, Feature Store)
  • 3+ years building and operating production MLOps pipelines — training, versioning, deployment, monitoring, rollback
  • Experience with SageMaker Unified Studio or Studio Classic — domain/project setup, blueprints, multi-tenant configuration
  • Infrastructure-as-Code with Terraform, CDK, or CloudFormation
  • IAM design for ML platforms — execution roles, service roles, cross-account access, Lake Formation, SSO/SAML
  • MLflow or equivalent experiment tracking
  • SageMaker Pipelines or similar workflow orchestration (Airflow, Step Functions)
  • Model serving — real-time endpoints, batch transform, auto-scaling, endpoint monitoring
  • Snowflake as a data source for ML pipelines
  • Kubernetes (EKS) and container orchestration
  • Networking and security — VPC, security groups, private endpoints, cross-account connectivity
 Added bonus if you have (Preferred):   
  • SageMaker Unified Studio domain provisioning, custom blueprints, project standardization
  • SageMaker Feature Store for online/offline feature management
  • SageMaker Model Monitor — data quality checks, bias detection, drift detection
  • AWS Machine Learning Specialty certification
About Us:
Founded in 2009, IntelliPro is a global leader in talent acquisition and HR solutions. Our commitment to delivering unparalleled service to clients, fostering employee growth, and building enduring partnerships sets us apart. We continue leading global talent solutions with a dynamic presence in over 160 countries, including the USA, China, Canada, Singapore, Japan, Philippines, UK, India, Netherlands, and the EU.
IntelliPro, a global leader connecting individuals with rewarding employment opportunities, is dedicated to understanding your career aspirations. As an Equal Opportunity Employer, IntelliPro values diversity and does not discriminate based on race, color, religion, sex, sexual orientation, gender identity, national origin, age, genetic information, disability, or any other legally protected group status. Moreover, our Inclusivity Commitment emphasizes embracing candidates of all abilities and ensures that our hiring and interview processes accommodate the needs of all applicants. Learn more about our commitment to diversity and inclusivity at https://intelliprogroup.com/.
Compensation: The pay offered to a successful candidate will be determined by various factors, including education, work experience, location, job responsibilities, certifications, and more. Additionally, IntelliPro provides a comprehensive benefits package, all subject to eligibility.
 

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