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Ml Infrastructure Jobs in Arizona (NOW HIRING)

Programmers.io

AI/ML Engineer

Scottsdale, AZ · On-site

Manage and optimize cloud-based ML infrastructure (GCP Vertex AI, AWS SageMaker, or equivalent). Implement CICD pipelines for ML and AI-driven applications. Monitor, troubleshoot, and optimize model ...

hyrusinc

Kubernetes/GCP Engineer

Scottsdale, AZ · On-site

$57.50 - $76.25/hr

... ML infrastructure on GCP Knowledge of Kubeflow, Vertex AI, or ML pipelines Experience integrating AI-driven automation into monitoring and incident response

ZipStaff Inc.

Kubernetes / GCP Engineer

Scottsdale, AZ · On-site

$48 - $50/hr

Experience with GPU-based workloads or ML infrastructure on GCP. * Knowledge of Kubeflow, Vertex AI, or ML pipelines. * Experience integrating AI-driven automation into monitoring and incident ...

Purple Drive

AI Engineer

Phoenix, AZ

Technical Skills: 6+ years of experience building large-scale distributed systems + strong experience with LLM systems, agentic workflows or advanced ML infrastructure, async processing, queues, and ...

IntraEdge

Senior AI/ML & IVR Engineer GCP

Scottsdale, AZ

$105K - $145K/yr

Knowledge of infrastructure-as-code (Terraform, Cloud Deployment Manager) Success in This Role: * Reliable, high-performance deployment of AI/ML and IVR solutions on GCP * Measurable improvements in ...

YO AI Labs

AI/ML Engineer - Remote

Phoenix, AZ · Remote

$200 - $350/hr

Remote Job Summary We are seeking an experienced AI/ML Engineer to build and deploy secure, scalable AI solutions for mission-critical initiatives while contributing to proprietary AI infrastructure.

Inficare Technologies

Lead AI Engineer

Phoenix, AZ · On-site

$99K - $131K/yr

What We're Looking For * 10+ years of experience building large-scale distributed systems + strong experience with LLM systems, agentic workflows or advanced ML infrastructure * Proven ownership of ...

Trinite Consulting Group LLC

AI Engineer

Scottsdale, AZ · On-site

$50/hr

Scottsdale, AZ - Hybrid Duration: Long Term Contract Pay: $50/hr on C2C About the Role We are seeking an experienced AIML Engineer to design, build, and operate AI/ML infrastructure and agentic ...

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Ml Infrastructure information

What is ML infrastructure?

ML Infrastructure refers to the underlying systems, tools, and processes that enable the development, deployment, and scaling of machine learning models. This includes data storage and management, computing resources, model training and serving environments, monitoring, and automation tools. ML Infrastructure ensures that data scientists and engineers can efficiently build, test, and maintain machine learning applications in a reliable and reproducible manner. It is a crucial foundation for organizations looking to operationalize AI and machine learning solutions at scale.

What are some common challenges faced by professionals working in ML infrastructure roles?

Professionals in ML Infrastructure often encounter challenges related to scaling systems to handle large volumes of data, ensuring reliable deployment pipelines, and maintaining reproducibility across different environments. They must also collaborate closely with data scientists and engineers to streamline workflows and address issues like version control and model monitoring. Staying updated with rapidly evolving tools and best practices is essential, and balancing stability with innovation is a frequent aspect of the role.

What are the key skills and qualifications needed to thrive as an ML infrastructure engineer, and why are they important?

To thrive as an ML Infrastructure Engineer, you need a strong background in software engineering, cloud computing, and machine learning concepts, often supported by a degree in computer science or a related field. Proficiency with containerization tools (like Docker and Kubernetes), cloud platforms (such as AWS, GCP, or Azure), and CI/CD systems is critical. Excellent problem-solving, collaboration, and communication skills help you efficiently work with data scientists and DevOps teams. These skills and qualities are vital for building scalable, reliable ML systems that support rapid experimentation and deployment in production environments.

What is the difference between Ml Infrastructure vs Data Engineer?

AspectML InfrastructureData Engineer
Required CredentialsBachelor's in CS, Data Science, or related; knowledge of cloud platformsBachelor's in CS, Software Engineering, or related; experience with databases and ETL tools
Work EnvironmentFocus on deploying and maintaining ML systems, cloud environments, and infrastructure toolsDesigning, building, and managing data pipelines and storage solutions
Industry UsageUsed in AI/ML teams to support model deployment and scalabilityUsed across data-driven organizations for data management and analytics

ML Infrastructure specialists focus on deploying, scaling, and maintaining machine learning systems and infrastructure, while Data Engineers primarily build and manage data pipelines and storage solutions. Both roles require technical skills and often collaborate, but their core responsibilities differ in focus and tools used.

What are popular job titles related to Ml Infrastructure jobs in Arizona?

For Ml Infrastructure jobs in Arizona, the most frequently searched job titles are:

What job categories do people searching Ml Infrastructure jobs in Arizona look for?

The top searched job categories for Ml Infrastructure jobs in Arizona are:

What cities in Arizona are hiring for Ml Infrastructure jobs?

Cities in Arizona with the most Ml Infrastructure job openings:

Ml Infrastructure jobs near you
Infographic showing various Ml Infrastructure job openings in Arizona as of August 2026, with employment types broken down into 90% Full Time, 7% Part Time, and 3% Contract. Highlights an 82% Physical, 6% Hybrid, and 12% Remote job distribution.

AI/ML Engineer

Programmers.io

Scottsdale, AZ • On-site

Contractor

Re-posted 4 days ago

Job description

Key Responsibilities:
Design, implement, and maintain ML pipelines for training, testing, and deploying AIML models.
Manage and optimize cloud-based ML infrastructure (GCP Vertex AI, AWS SageMaker, or equivalent).
Implement CICD pipelines for ML and AI-driven applications.
Monitor, troubleshoot, and optimize model performance and system reliability.
Automate workflows for data ingestion, model training, deployment, and monitoring.
Collaborate with cross-functional teams to ensure secure, scalable, and compliant ML operations.
Apply MLOps best practices for reproducibility, versioning, and governance of ML models.
Required Qualifications:
5 years experience in DevOps, CloudOps, or ML Ops.
5 years experience with GCP AIML services (Vertex AI, AI Platform, BigQuery ML) or AWS ML services (SageMaker etc).
5 years Experience with containerization and orchestration (Docker, Kubernetes).
Proficiency in infrastructure-as-code (Terraform, CloudFormation, or Deployment Manager). Familiarity with CICD pipelines (Jenkins, GitHub Actions, GitLab CI, or ArgoCD).
Strong programming skills in Python, Bash, or Go, with experience in ML frameworks (TensorFlow, PyTorch, Scikit-learn).
Preferred Certifications (one or more):
Google Cloud Professional Machine Learning Engineer
Google Cloud Professional Data Engineer
AWS Certified Machine Learning Specialty
Certified Kubernetes Admin(CKA)
Google Professional Cloud Architect