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Amazon Sagemaker Jobs in Texas (NOW HIRING)

IVID TEK INC

MLOps Platform Engineer (SageMaker)

Plano, TX · On-site

$123.98 - $130.87/hr

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

Calance US

MLOps Platform Engineer (SageMaker)

Plano, TX

  • Medical

  • Dental

  • Vision

  • Life

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

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 ...

Plugins Inc

Technical Project Manager

Dallas, TX · On-site

Amazon S3 • AWS Glue • Amazon Redshift • Amazon EMR • AWS Lambda • Amazon SageMaker • Amazon Athena • AWS Step Functions Additional responsibilities include: * Partner with Data ...

Tiger Analytics

Lead Data Engineer - AWS

Dallas, TX · On-site

$113K - $136K/yr

Architect data pipelines using Amazon Bedrock and Amazon SageMaker to build, deploy, and scale Generative AI applications • Vector Foundations: Implement and optimize vector search capabilities ...

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$21.4K

$71.9K

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How much do amazon sagemaker jobs pay per year?

As of Aug 19, 2026, the average yearly pay for amazon sagemaker in Texas is $71,858.00, according to ZipRecruiter salary data. Most workers in this role earn between $54,000.00 and $89,400.00 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.

What are popular job titles related to Amazon Sagemaker jobs in Texas?

For Amazon Sagemaker jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Amazon Sagemaker jobs in Texas look for?

The top searched job categories for Amazon Sagemaker jobs in Texas are:

Amazon Sagemaker jobs near you
Infographic showing various Amazon Sagemaker job openings in Texas as of August 2026, with employment types broken down into 91% Full Time, 6% Part Time, and 3% Contract. Highlights an 84% Physical, 6% Hybrid, and 10% Remote job distribution, with an average salary of $71,858 per year, or $34.5 per hour.

MLOps Platform Engineer (SageMaker)

Judge Group, Inc.

Plano, TX • On-site

$85 - $95/hr

Other

Posted yesterday

New

Job description

Location: Plano, TX Salary: $85.00 USD Hourly - $95.00 USD Hourly Description:
Job Title: MLOps Platform Engineer (SageMaker)
Location: Plano, TX
Contract


Must Haves:
  • 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 Classic 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
  • MLflow or equivalent experiment tracking
  • SageMaker Pipelines or similar workflow orchestration (Airflow, Step Functions)
  • Unified Studio is preferred to have but Classic is must have.

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
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This job and many more are available through The Judge Group. Please apply with us today!

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