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Aws Sagemaker Jobs (NOW HIRING)

SWBC

Senior Data Scientist

San Antonio, TX · On-site

  • Medical

  • Retirement

Your daily work will center on developing and deploying within SWBC Intelligence, our multi-model AI orchestration platform spanning AWS Bedrock, AWS Sagemaker, alongside Hex for experimentation and ...

SWBC

Senior Data Scientist

San Antonio, TX · On-site

  • Medical

  • Retirement

Your daily work will center on developing and deploying within SWBC Intelligence, our multi-model AI orchestration platform spanning AWS Bedrock, AWS Sagemaker, alongside Hex for experimentation and ...

SWBC

Senior Data Scientist

San Antonio, TX · On-site

$140 - $210/hr

  • Medical

  • Retirement

Your daily work will center on developing and deploying within SWBC Intelligence, our multi-model AI orchestration platform spanning AWS Bedrock, AWS Sagemaker, alongside Hex for experimentation and ...

Swbc

Senior Data Scientist

San Antonio, TX

  • Medical

  • Retirement

Your daily work will center on developing and deploying within SWBC Intelligence, our multi-model AI orchestration platform spanning AWS Bedrock, AWS Sagemaker, alongside Hex for experimentation and ...

SWBC

Data Scientist II

San Antonio, TX · On-site

  • Medical

  • Retirement

Build and maintain ML/AI experiment workflows using Hex for prototyping and exploration, and AWS SageMaker and MLflow for experiment tracking, feature engineering, and model versioning. * Develop and ...

SWBC

Data Scientist II

San Antonio, TX

  • Medical

  • Retirement

Build and maintain ML/AI experiment workflows using Hex for prototyping and exploration, and AWS SageMaker and MLflow for experiment tracking, feature engineering, and model versioning. * Develop and ...

Swbc

Data Scientist II

San Antonio, TX

  • Medical

  • Retirement

Build and maintain ML/AI experiment workflows using Hex for prototyping and exploration, and AWS SageMaker and MLflow for experiment tracking, feature engineering, and model versioning. * Develop and ...

Universal Business Consulting

AI Ops

Dallas, TX · On-site

$129K - $165K/yr

Our client is looking for a Senior ML Platform Engineer to design, build, and operationalize an enterprise ML platform on AWS SageMaker Unified Studio. The successful candidate will play a key role ...

SWBC

Data Scientist II

San Antonio, TX · On-site

... AWS SageMaker and MLflow for experiment tracking, feature engineering, and model versioning. • Develop and deploy AI/ML solutions within SWBC Intelligence, the enterprise multi-model AI ...

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All JobsAws Sagemaker Jobs

Aws Sagemaker information

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How much do aws sagemaker jobs pay per hour?

As of Aug 14, 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 for an AWS SageMaker?

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 does an AWS SageMaker do?

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?

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 August 2026, with employment types broken down into 72% Full Time, 5% Part Time, 2% Temporary, and 21% Contract. Highlights an 81% In-person, 5% Hybrid, and 14% Remote job distribution, with an average salary of $112,422 per year, or $54 per hour.

MLOPS Engieer OR ML Engineer

Texas State Library and Archives Commision

East Hanover, NJ • On-site

Contractor

Re-posted 18 days ago

Job description

MLOPS Engieer OR ML Engineer with DevOps
East Hanover, NJ
12 Months Contract
Job Description:
1. 8 to 10 years of industry experience working in Software Engineering, DevOps or Data Engineering with Data Science and MLOps experience.
2. Strong DevOps, Data Engineering and Client background with AWS Experience with one or more of MLOps tools: DataIKU, ModelDB, Kubeflow, Pachyderm, and Data Version Control (DVC) etc. Experience in Distributed computing, Data pipelines, and AI/Client.
3. Experience in DataIKU, Data Bricks, and Azure Kubernetes service.
4. Extensive experience with Unix/AIX/Linux environments
5. Experience with automation servers such as Jenkins, CloudBees, Travis, Gitlab actions
6. Experience with logging tools such as Splunk, ElasticSearch, Kibana, Logstash
7. Familiarity with setting up Hyperparameter Tuning tools like DataIKU/ kubeflow/AWS Sagemaker or similar
8. Familiarity with setting up model and experiment Versioning technologies like MLFLow/Kubeflow/AWS Sagemaker or similar
9. Familiarity with JIRA and SNOW process.
Skills:
Preferred: DataIKU, Data Bricks, DevOps Tools like Jenkins, Gitlab
Good to have: Azure Kubernetes service, AWS Sagemaker/MLFlow
Roles & Responsibilities:
1. Ensure reliability and cost saving. Scale the proof of concept product to enterprise grade application with all the required components for reliability, scalability, monitoring and security.
2. Suggest and implement the best practices from Software engineering to ML workflow to ensure CI/CD, reproducibility and quick delivery cycle.
3. Suggest and implement best governance process and user access management
4. Lead and drive the deployment of ML models, life cycle management and monitoring of Machine Learning(ML) and Deep Learning (DL) models in in all stages leading to production
5. Be a subject matter expert on DevOps practices, CI/CD and Configuration Management with assigned engineering team
6. Automate and streamline ML operations and processes.
7. Build and maintain tools for deployment, monitoring, and operations. Also troubleshoot and resolve issues in development, testing, and production environments
8. Operate and maintain systems supporting the provisioning of new clients, applications, and features
9. Work to improve Data scientist and Data engineer productivity and delivery speed by enabling them to be more self-sufficient with automated operational processes.
10. Successfully devise and implement strategies to ensure ML heavy systems operate with high accuracy in Production and adapt to discovered needs.
11. Collaborate with Data Scientists, Data Engineers, ML Engineers, cloud platform and application engineers to create and implement cloud policies and governance for ML/DL model life cycle
12. Consumer ticket triage and assignment(SNOW support)
13. Co-ordinate with required teams for ticket resolution, exploration and automation

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