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Amazon Machine Learning Jobs in Arizona (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 ...

Deloitte

AI Solutions Architect

Tempe, AZ · On-site

$60.25 - $79.50/hr

Experience with at least one cloud platform, such as Amazon Web Services, Microsoft Azure, or Google Cloud Platform, and artificial intelligence and machine learning tools and frameworks * Ability to ...

Deloitte

Finance Analytics & AI Senior Consultant

Tempe, AZ · On-site

$111K/yr

Experience using Python and Structured Query Language (SQL) to develop analytics, machine learning, data engineering, or automation solutions in Amazon Web Services (AWS), Google Cloud Platform (GCP ...

Deloitte

Lead Business Transformation Architect

Tempe, AZ · On-site

$53 - $72.50/hr

Qualifications Required: * 2+ years of experience with artificial intelligence or machine learning concepts and algorithms * 5+ years of experience with Amazon Web Services, Microsoft Azure, Google ...

Deloitte

Manager - Data Science / Data Lake

Tempe, AZ · On-site

... or support machine learning workflows * Experience working with cyber security cloud platforms such as Google SecOps, Amazon Web Services (AWS), or Microsoft Azure, and exposure to security ...

Deloitte

Oracle Gen AI Manager

Tempe, AZ

... machine learning initiatives * Identify high-value AI use cases and guide teams on prompt ... Experience working across OCI and at least one additional cloud platform, including Amazon Web ...

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Amazon Machine Learning information

See Arizona salary details

$23.8K

$39.7K

$82K

How much do amazon machine learning jobs pay per year?

As of Jul 14, 2026, the average yearly pay for amazon machine learning in Arizona is $39,683.00, according to ZipRecruiter salary data. Most workers in this role earn between $30,300.00 and $42,900.00 per year, depending on experience, location, and employer.

What types of projects and daily tasks can I expect in an Amazon Machine Learning position?

As an Amazon Machine Learning professional, your daily work may involve designing and deploying machine learning models, analyzing large datasets, and collaborating with cross-functional teams such as data engineers and product managers. You’ll frequently participate in code reviews, troubleshoot complex algorithms, and help optimize model performance for various Amazon products and services. Projects often range from natural language processing and recommendation systems to forecasting and computer vision initiatives. This dynamic environment offers exposure to cutting-edge innovation and opportunities to grow your technical and leadership skills within a global technology leader.

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

To excel in an Amazon Machine Learning role, you should possess strong expertise in machine learning algorithms, statistical analysis, programming (Python, Java, or Scala), and typically hold a degree in computer science, engineering, or a related field. Familiarity with AWS cloud services (like SageMaker, EC2, S3), big data frameworks, and relevant certifications such as AWS Certified Machine Learning are highly valuable. Effective communication, problem-solving skills, and the ability to work collaboratively in diverse teams help distinguish top candidates. These skills are crucial for developing scalable AI solutions, translating business problems into technical models, and successfully integrating them into Amazon’s large-scale operations.

What is an Amazon Machine Learning job?

An Amazon Machine Learning job involves developing, deploying, and optimizing machine learning models to improve products and services within Amazon. Professionals in this role work with large-scale data, build predictive models, and collaborate with engineering and business teams to drive data-driven decisions. Responsibilities may include data preprocessing, feature engineering, model training, and deploying machine learning solutions in production. Strong programming skills, proficiency in ML frameworks, and experience with AWS services like SageMaker are often required.

What are popular job titles related to Amazon Machine Learning jobs in Arizona? For Amazon Machine Learning jobs in Arizona, the most frequently searched job titles are:
What job categories do people searching Amazon Machine Learning jobs in Arizona look for? The top searched job categories for Amazon Machine Learning jobs in Arizona are:
Amazon Machine Learning jobs near you
ML Ops Engineer

ML Ops Engineer

Sahi Softtech

Phoenix, AZ • On-site

Other

Posted 5 days ago

Job description

We are looking for an experienced MLOps Engineer to design, build, deploy, and maintain scalable machine learning operations pipelines on AWS. The ideal candidate will work closely with data scientists, machine learning engineers, data engineers, and DevOps teams to productionize AI/ML models, automate deployment workflows, and ensure models are reliable, scalable, secure, and well-monitored in production environments.

Key Responsibilities:

Design, build, and maintain end-to-end MLOps pipelines for model training, validation, deployment, monitoring, and retraining.

Develop and automate CI/CD pipelines for machine learning models and related services using tools such as AWS CodePipeline, AWS CodeBuild, Jenkins, GitLab CI/CD, or GitHub Actions.

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 batch, real-time, and event-driven inference use cases.

Implement model versioning, experiment tracking, artifact management, and reproducibility using tools such as Amazon SageMaker Model Registry, MLflow, or similar platforms.

Containerize ML applications and services using Docker and deploy them using Kubernetes, Amazon EKS, or Amazon ECS.

Collaborate with data scientists and AI/ML engineers to move machine learning models from development to production.

Monitor production models for performance, accuracy, latency, data drift, model drift, and system reliability.

Build automation for model retraining, validation, approval workflows, and production deployment.

Work with AWS data and storage services such as Amazon S3, Amazon Redshift, AWS Glue, Amazon Athena, Amazon RDS, and DynamoDB as needed.

Implement infrastructure as code using Terraform, AWS CloudFormation, or AWS CDK.

Ensure security, access control, compliance, and governance using AWS IAM, VPC, CloudWatch, CloudTrail, KMS, and related AWS services.

Troubleshoot and resolve issues related to ML pipelines, cloud infrastructure, deployments, data pipelines, and production model performance.

Document MLOps processes, deployment standards, monitoring practices, and operational runbooks.

Required Skills and Qualifications:

Bachelor’s degree in Computer Science, 

Engineering, Data Science, Information Technology, or a related field.

Strong experience in MLOps, DevOps, machine learning engineering, cloud engineering, or platform engineering.

Hands-on experience with AWS cloud services, especially Amazon SageMaker, S3, Lambda, ECS, EKS, IAM, CloudWatch, and related services.

Strong programming experience with Python.

Experience with machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, XGBoost, or similar.

Hands-on experience building CI/CD pipelines and automating deployment workflows.

Strong knowledge of Docker, containerization, and container orchestration using Kubernetes, Amazon EKS, or Amazon ECS.

Experience with model deployment patterns, including real-time inference, batch inference, and API-based model serving.

Familiarity with ML lifecycle tools such as SageMaker Pipelines, SageMaker Model Registry, MLflow, Kubeflow, or DVC.

Experience with infrastructure as code tools such as Terraform, CloudFormation, or AWS CDK.

Good understanding of model monitoring, data drift, model drift, logging, alerting, and production support.

Knowledge of version control tools such as Git.

Strong troubleshooting, analytical, communication, and collaboration skills.

Preferred Qualifications:

AWS certification such as AWS Certified Machine Learning – Specialty, AWS Certified Solutions Architect, or AWS Certified DevOps Engineer.

Experience with data engineering tools such as AWS Glue, Apache Spark, Airflow, Kafka, or Databricks.

Experience with feature stores, model registries, automated retraining pipelines, and model governance.

Understanding of security best practices for cloud-based ML environments.

Experience working in Agile/Scrum development environments.

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