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Ai Reliability Engineer Jobs in Arizona (NOW HIRING)

SF Demo - Rio Tinto

Reliability Engineer

Superior, AZ

$97K - $122K/yr

The Reliability Engineer ensures maintenance and reliability programs comply with applicable MSHA ... Experience utilizing AI tools. What we offer We are committed to providing our employees with a ...

New

Apex Systems

Site Reliability Engineer III

Chandler, AZ · On-site

$65 - $70/hr

Partner with platform engineering, networking, security, and AI infrastructure teams to embed reliability into solutions. * Support the consistent onboarding and sustained operation of enterprise ...

Apex Systems

Site Reliability Engineer III

Chandler, AZ · On-site

$58.25 - $77.25/hr

Job#: 3046883 Site Reliability Engineer III Location: Chandler, Arizona (Hybrid) Duration: 12 ... By applying for this job, you agree to receive calls, AI-generated calls, text messages, or emails ...

Apex Systems

Site Reliability Engineer II

Chandler, AZ · On-site

$82.66 - $90.92/hr

Site Reliability Engineer II**Location:** Chandler, Arizona (Hybrid)Duration: 12 monthsRole ... By applying for this job, you agree to receive calls, AI-generated calls, text messages, or emails ...

Fox

Senior Engineer, AI Site Reliability

Tempe, AZ · On-site

$114K - $165K/yr

Fox is hiring a Senior Engineer, AI Site Reliability to help build and operate infrastructure and platforms to support APIs around our live direct to consumer APIs for major live events such as the ...

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Ai Reliability Engineer information

What is an AI reliability engineer?

AI Reliability Engineers are professionals responsible for ensuring that artificial intelligence systems function reliably, safely, and effectively over time. They work on monitoring AI models in production, identifying and mitigating potential failures, and improving the robustness of AI systems. Their tasks often include testing, validation, performance monitoring, and implementing best practices for maintaining AI infrastructure. By focusing on reliability, they help organizations deploy AI solutions that are dependable and trustworthy in real-world environments.

What are some common challenges AI reliability engineers face when ensuring model robustness in production environments?

Ai Reliability Engineers often encounter challenges such as monitoring AI model performance for drift or unexpected behavior, managing data quality issues, and implementing automated alerting systems for anomalies. In production, it's crucial to ensure that AI models operate consistently and remain reliable under varying conditions and data inputs. Collaborating closely with data scientists, software engineers, and DevOps teams is essential to address these challenges and to continuously improve model reliability and uptime.

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

To thrive as an AI Reliability Engineer, you need a solid background in computer science or engineering, expertise in AI/ML concepts, and experience with software testing and reliability methodologies. Familiarity with tools like TensorFlow, PyTorch, CI/CD pipelines, and reliability testing frameworks, along with certifications in cloud platforms (e.g., AWS Certified Machine Learning), is highly valuable. Analytical thinking, problem-solving abilities, and strong collaboration skills set top performers apart in this role. These skills ensure robust, dependable AI systems that meet performance standards and maintain trust in critical applications.

What is the difference between Ai Reliability Engineer vs Data Scientist?

AspectAi Reliability EngineerData Scientist
Required CredentialsBachelor's or master's in CS, engineering, or related; certifications in AI/MLBachelor's or master's in CS, statistics, or related; certifications in data analysis or ML
Work EnvironmentTech companies, AI-focused teams, engineering departmentsResearch labs, tech firms, analytics teams
Employer & Industry UsageAI product development, machine learning systems, reliability testingData analysis, predictive modeling, business insights

While both roles involve AI and ML, Ai Reliability Engineers focus on ensuring AI system robustness and uptime, whereas Data Scientists analyze data to generate insights and models. The roles often collaborate but serve different primary functions within AI projects.

What are popular job titles related to Ai Reliability Engineer jobs in Arizona?

For Ai Reliability Engineer jobs in Arizona, the most frequently searched job titles are:

What job categories do people searching Ai Reliability Engineer jobs in Arizona look for?

The top searched job categories for Ai Reliability Engineer jobs in Arizona are:

What cities in Arizona are hiring for Ai Reliability Engineer jobs?

Cities in Arizona with the most Ai Reliability Engineer job openings:

Ai Reliability Engineer jobs near you

Site Reliability Engineering(SRE) - Remote

Phoenix, AZ • Remote

YO AI Labs
See more jobs at YO AI Labsexternal

$30 - $80/hr

Full-time

Posted 2 days ago

New

Job description

Developer & Infrastructure Expert

Role Type: Contractor
Location: Remote

Job Overview

We are seeking experienced Developer & Infrastructure Experts to evaluate AI-powered workflows across software development, cloud infrastructure, DevOps, SRE, and platform engineering. You will test AI-generated commands, configurations, and workflows and provide practical feedback based on real-world engineering standards.

No prior AI experience is required—your technical expertise and infrastructure knowledge are what matter most.

Key Responsibilities
  • Test and validate AI-assisted workflows across DevOps, cloud, infrastructure, and platform engineering.
  • Evaluate AI-generated commands, configurations, scripts, and deployment workflows for accuracy and reliability.
  • Work with AWS, Azure, GCP, Kubernetes, Docker, Terraform, and CI/CD environments.
  • Review workflows involving GitHub, GitLab, Azure DevOps, and JIRA.
  • Assess AI agents and workplace connectors across tools such as Slack, Google Drive, and Microsoft 365.
  • Identify technical errors, security or reliability concerns, and workflow gaps.
  • Document findings and provide clear, actionable feedback.
  • Recommend improvements based on real-world engineering practices and industry standards.
  • Collaborate remotely with project stakeholders through written evaluations and virtual discussions.
Required Skills
  • Azure DevOps
  • Cloud Infrastructure
  • Site Reliability Engineering (SRE)
  • Platform Engineering
  • AWS / Azure / GCP
  • Kubernetes / Docker
  • Terraform
  • CI/CD
  • GitHub / GitLab / JIRA
  • Observability
  • ChatGPT Work
  • Claude Code
  • Claude Cowork
  • AI Agents
  • Workplace Connectors
Preferred Qualifications
  • Professional experience in software engineering, DevOps, SRE, cloud, platform, or infrastructure engineering.
  • Hands-on experience with major cloud platforms, Kubernetes, Docker, Terraform, CI/CD, and observability tools.
  • Experience with developer workflow and project management platforms such as GitHub, GitLab, Azure DevOps, and JIRA.
  • Experience using AI agents such as ChatGPT, Claude Code, Claude Cowork, or Codex in development or infrastructure workflows.
  • Strong ability to evaluate technical processes and provide actionable feedback.
  • Excellent written and verbal communication skills.
  • Experience with workplace connectors, automation, or AI-powered infrastructure workflows is a plus.