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Aws Machine Learning Jobs in Arizona (NOW HIRING)

Lorven Technologies

AI Solution Architect

Tempe, AZ · On-site

$60.25 - $79.50/hr

AWS Machine Learning Specialty * Terraform Associate * Experience in regulated industries. * Experience designing AI systems under compliance constraints. Core Competencies * Strategic Architecture ...

Programmers.io

AI/ML Engineer

Scottsdale, AZ · On-site

Google Cloud Professional Machine Learning Engineer Google Cloud Professional Data Engineer AWS Certified Machine Learning Specialty Certified Kubernetes Admin(CKA) Google Professional Cloud ...

Universal Avionics

AI/ML Engineering Intern

Tucson, AZ · On-site

$14.50 - $18.75/hr

Design and develop scalable AI solutions using machine learning models and tools * Build and optimize data pipelines, prototypes, and training datasets using cloud platforms (AWS, Azure, or GCP)

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How much do aws machine learning jobs pay per hour?

As of Jun 14, 2026, the average hourly pay for aws machine learning in Arizona is $65.29, according to ZipRecruiter salary data. Most workers in this role earn between $58.03 and $76.15 per hour, depending on experience, location, and employer.

What is an AWS Machine Learning job?

An AWS Machine Learning job involves designing, building, and deploying machine learning models using Amazon Web Services (AWS) cloud infrastructure. Professionals in this role work with services like Amazon SageMaker, AWS Lambda, and AWS Glue to develop AI-driven applications. They optimize models for scalability, integrate them into cloud-based systems, and ensure efficient data processing. Strong knowledge of machine learning algorithms, AWS architecture, and MLOps best practices is essential for success in this role.

What are the key skills and qualifications needed to thrive in the Aws Machine Learning position, and why are they important?

To thrive as an AWS Machine Learning professional, you need a strong understanding of machine learning principles, proficiency in programming languages like Python, and experience with AWS cloud services such as SageMaker. AWS Certified Machine Learning certification and familiarity with data pipelines, EC2, and Lambda are commonly required. Strong problem-solving, communication, and teamwork skills help you translate business requirements into technical solutions and collaborate effectively with diverse stakeholders. These skills are essential to efficiently deploy and manage scalable machine learning models that deliver business value in cloud-based environments.

What are some typical responsibilities for someone working in an AWS Machine Learning role?

In an AWS Machine Learning position, you'll typically design, develop, and deploy machine learning models using AWS services like SageMaker, Glue, and Lambda. Daily tasks often include data preprocessing, building and training models, and optimizing performance for production environments. You'll collaborate closely with data engineers, software developers, and business analysts to translate business needs into technical solutions. The role may also involve monitoring deployed models, managing cloud resources, and staying updated on new AWS features to ensure efficient and scalable machine learning workflows.

What are the most commonly searched types of Aws Machine Learning jobs in Arizona? The most popular types of Aws Machine Learning jobs in Arizona are:
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Aws Machine Learning jobs near you
Infographic showing various Aws Machine Learning job openings in Arizona as of June 2026, with employment types broken down into 82% Full Time, and 18% Contract. Highlights an 64% In-person, 9% Hybrid, and 27% Remote job distribution, with an average salary of $135,799 per year, or $65.3 per hour.
Senior Machine Learning Engineer

Senior Machine Learning Engineer

Prime Solutions Group, Inc.

Goodyear, AZ • On-site

$110K/yr

Full-time

Posted yesterday

Job description

Job Type
Full-time
Description
Prime Solutions Group (PSG), Inc. is an innovative digital engineering company founded in 2007 and headquartered in Goodyear, AZ. We specialize in advanced sensing, AI/ML, and digital engineering solutions, partnering with many of the nation's leading defense companies to deliver mission-critical technology.
Our work spans the full system lifecycle-from R&D to operational deployment-supporting the Department of Defense, Intelligence Community, and federal partners. At PSG, you'll join a small, agile team where your contributions have a direct impact while working alongside top-tier engineering talent.
Position Overview
Turn machine learning into real-world mission capability.
PSG is seeking a Machine Learning Engineer to design, build, and deploy AI/ML solutions that power mission-critical systems. This role focuses on taking models from concept to production-developing pipelines, integrating models into software systems, and ensuring performance, scalability, and reliability in real-world environments.
You'll work at the intersection of machine learning, software engineering, and DevSecOps, collaborating with cross-functional teams to deliver secure, production-ready AI solutions supporting national security missions.
What You'll Do
  • Design, build, and maintain ML pipelines for data preparation, training, evaluation, and deployment
  • Develop and optimize ML models and applications using Python and frameworks like PyTorch or TensorFlow
  • Integrate models into production systems (APIs, batch pipelines, real-time services)
  • Implement model validation, evaluation metrics, and performance monitoring
  • Improve model accuracy, scalability, and efficiency through tuning and data strategy improvements
  • Collaborate with data engineers and domain experts to prepare and validate datasets
  • Partner with DevSecOps/MLOps teams to deploy ML solutions in secure environments
  • Troubleshoot model and pipeline issues; perform root cause analysis and optimization
  • Contribute to technical documentation, test plans, and operational runbooks
  • Participate in design reviews, architecture discussions, and Agile development processes
  • Mentor junior engineers and promote engineering best practices

Requirements
  • U.S. Citizenship
  • Active Top Secret Clearance (SCI eligibility; CI Poly preferred or ability to obtain)
  • Bachelor's degree in Computer Science, Engineering, Data Science, or related field
  • 4+ years of experience in:
    • Machine Learning Engineering
    • Applied AI/ML development
    • Production ML systems
  • Strong Python skills and experience with ML libraries (NumPy, pandas, scikit-learn, PyTorch, TensorFlow)
  • Experience developing, training, and deploying ML models in real-world applications
  • Solid understanding of the ML lifecycle (data ? training ? validation ? deployment ? monitoring)
  • Experience building maintainable, production-quality software
  • Familiarity with Docker and cloud environments (AWS, Azure, or GCP)
  • Experience working in Agile and CI/CD environments
  • Strong problem-solving, communication, and collaboration skills

Preferred Qualifications
  • Master's degree in a related field
  • Experience with computer vision, image/video analytics, or sensor data (e.g., RF, SAR)
  • Experience transitioning models from research to production environments
  • Familiarity with experiment tracking, model versioning, and reproducibility practices
  • Experience with GPU-based ML workflows and cloud ML platforms
  • Background in defense, intelligence, or other regulated environments

Why Join PSG?
At PSG, you're not just taking a job-you're building technology that matters.
  • Competitive compensation & benefits
  • 9/80 flexible work schedule
  • Professional development & tuition assistance
  • Small, agile team with high ownership and visibility
  • Work on mission-critical systems supporting national security
  • Opportunities to grow across AI/ML, software engineering, and platform development

Bring your machine learning expertise to PSG and help deliver the next generation of secure, intelligent, mission-driven systems.
Salary Description
Salary range starts at $110,000 with the potential for higher compensation based on experience, skills, and mission needs.

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