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Ai Monitoring Jobs in Arizona (NOW HIRING)

Principal AI Engineer

Tucson, AZ · On-site

$179 - $226/hr

Establish platform capabilities that enable experimentation, model deployment, monitoring, observability, and continuous improvement. * Partner with IT and enterprise technology teams to ensure AI ...

Principal AI Engineer

Tucson, AZ · On-site

$179K - $226K/yr

Establish platform capabilities that enable experimentation, model deployment, monitoring, observability, and continuous improvement. * Partner with IT and enterprise technology teams to ensure AI ...

Establish platform capabilities that enable experimentation, model deployment, monitoring, observability, and continuous improvement. * Partner with IT and enterprise technology teams to ensure AI ...

Principal AI Engineer

Tucson, AZ · On-site

$179K - $226K/yr

Establish platform capabilities that enable experimentation, model deployment, monitoring, observability, and continuous improvement. * Partner with IT and enterprise technology teams to ensure AI ...

Establish best practices for model lifecycle management: versioning, monitoring, retraining, and ... AI/ML: Strong understanding of LLMs, RAG architectures, agentic AI systems, prompt engineering ...

New

Monitor and analyze developments in AI law and regulation and translate them into practical, client-ready guidance; * Support AI-related corporate and technology transactions, including due diligence ...

Gen AI Engineer II

Phoenix, AZ · On-site

$97K - $133K/yr

Monitor and analyze the performance of AI tools, providing feedback to improve their accuracy and usability. * Ensure that AI-generated code adheres to organizational coding standards and best ...

Principal AI Engineer

Phoenix, AZ · On-site

$180 - $230/hr

Establish best practices for model lifecycle management: versioning, monitoring, retraining, and ... AI/ML: Strong understanding of LLMs, RAG architectures, agentic AI systems, prompt engineering ...

Showing results 41-60

Ai Monitoring information

What is AI monitoring?

AI monitoring refers to the process of continuously observing and analyzing artificial intelligence systems to ensure they operate as intended. This includes tracking performance, detecting anomalies, ensuring compliance with ethical guidelines, and identifying potential biases or errors. Effective AI monitoring helps organizations maintain transparency, improve system reliability, and ensure that AI models make fair and accurate decisions. It is essential in applications where AI impacts critical business or societal outcomes.

What are some common challenges faced by professionals in AI monitoring roles, and how can they be addressed?

Professionals in AI Monitoring often encounter challenges such as managing large volumes of data, identifying and responding to atypical model behavior, and ensuring compliance with ethical and regulatory standards. Staying updated on the latest AI trends and best practices, utilizing robust monitoring tools, and collaborating closely with data scientists and engineers can help address these challenges. Regular training and open communication within cross-functional teams are also essential to maintain effective oversight and quickly mitigate potential issues.

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

To thrive as an AI Monitoring Specialist, you need a solid understanding of data analysis, machine learning concepts, and system monitoring, often supported by a degree in computer science or a related field. Familiarity with monitoring platforms like Datadog, Prometheus, or Splunk, as well as experience with scripting languages and AI model management tools, is typically required. Attention to detail, critical thinking, and strong communication skills help specialists identify issues quickly and collaborate with technical teams. These skills and qualities are crucial for ensuring AI systems operate reliably, securely, and efficiently in real-world applications.

What is the difference between Ai Monitoring vs Data Analyst?

AspectAi MonitoringData Analyst
Required CredentialsTypically requires knowledge of AI systems, programming, and data analysis toolsRequires statistical, analytical, and data visualization skills, often with a degree in data science or related fields
Work EnvironmentOften involves monitoring AI systems in real-time, using specialized software, in tech or AI-focused companiesAnalyzes data sets, creates reports, and provides insights, working in various industries like finance, marketing, or healthcare
Employer & Industry UsageCommon in AI development firms, tech companies, and organizations deploying AI solutionsWidely used across industries for decision-making, reporting, and strategic planning

While both roles involve working with data, Ai Monitoring focuses on overseeing AI system performance and ensuring operational accuracy, whereas Data Analysts interpret data to support business decisions. Understanding these differences helps in choosing the right career path or job search focus.

Infographic showing various Ai Monitoring job openings in Arizona as of August 2026, with employment types broken down into 1% As Needed, 83% Full Time, 10% Part Time, 2% Temporary, and 4% Contract. Highlights an 92% Physical, 3% Hybrid, and 5% Remote job distribution.

Job description

NOIRLab is seeking an accomplished Principal AI Engineer to define and lead the organization's artificial intelligence strategy, technical architecture, and AI engineering capabilities. This highly visible leadership role will establish the foundational AI platforms, engineering practices, and governance framework that enable scientists, software engineers, and operations teams to harness the power of artificial intelligence in support of groundbreaking astronomical research and observatory operations.

As NOIRLab's first dedicated AI engineering leader, you will have a unique opportunity to shape how AI is adopted across one of the world's premier astronomical research organizations. You will identify and prioritize high-impact AI opportunities, build enterprise-scale AI capabilities, and deliver innovative solutions that accelerate scientific discovery, increase engineering productivity, and improve operational efficiency.

This role combines strategic vision with hands-on technical leadership. You will partner closely with researchers, software engineers, data scientists, and operational stakeholders to design, prototype, implement, and deploy production-ready AI systems. The ideal candidate is equally comfortable defining long-term AI strategy, architecting scalable AI platforms, and personally contributing to the development of advanced AI solutions.

At NOIRLab, we believe scientific discovery and technological innovation go hand in hand. We value intellectual curiosity, continuous learning, and a passion for solving complex problems. This position offers the rare opportunity to apply cutting-edge AI technologies—including machine learning, generative AI, large language models (LLMs), advanced analytics, and agentic systems—to help answer fundamental questions about our universe while advancing the future of scientific computing.

If you are energized by the challenge of building AI capabilities from the ground up and motivated by the potential to make a lasting impact on science, this is an exceptional opportunity to lead transformative innovation at a global research institution.

Key ResponsibilitiesAI Strategy & Technical Leadership
  • Define and execute NOIRLab's enterprise AI strategy, technical vision, and multi-year roadmap aligned with scientific, engineering, and operational goals.
  • Identify, evaluate, and prioritize high-value AI use cases that deliver measurable business and scientific impact.
  • Establish AI engineering standards, development methodologies, reusable frameworks, and best practices that support scalable adoption across the organization.
  • Assess and implement emerging technologies, including generative AI, foundation models, LLMs, AI agents, and advanced machine learning approaches.
  • Develop governance frameworks, model evaluation processes, and lifecycle management practices that ensure responsible, secure, and effective AI deployment.
  • Establish key performance indicators (KPIs) and success metrics to evaluate the effectiveness of AI initiatives.
AI Platform Architecture & Solution Development
  • Design and implement secure, scalable, and production-ready AI platforms and applications that integrate with scientific research systems, software platforms, and operational workflows.
  • Lead the development of LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, AI agents, copilots, and other advanced AI solutions.
  • Establish platform capabilities that enable experimentation, model deployment, monitoring, observability, and continuous improvement.
  • Partner with IT and enterprise technology teams to ensure AI systems meet organizational requirements for reliability, scalability, security, and compliance.
Advanced Analytics & Scientific Computing
  • Develop advanced analytics capabilities that support scientific discovery, operational decision-making, and organizational effectiveness.
  • Apply machine learning, statistical modeling, and AI techniques to large-scale scientific datasets and computational workflows.
  • Evaluate and optimize model performance, accuracy, explainability, and operational effectiveness.
  • Collaborate with researchers and technical teams to leverage AI in solving complex scientific and engineering challenges.
Technical Leadership & Cross-Functional Collaboration
  • Serve as the organization's senior technical authority for AI, machine learning, and advanced analytics.
  • Mentor engineers, scientists, and technical teams in AI technologies, architecture, and responsible AI practices.
  • Collaborate across scientific, engineering, and operations organizations to drive AI adoption and innovation.
  • Engage with external research institutions, observatories, and scientific communities to promote collaboration, interoperability, and knowledge sharing.
  • Foster a culture of experimentation, continuous improvement, and responsible innovation.
What You'll BringRequired Qualifications
  • Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Software Engineering, or a related technical field, or equivalent combination of education and experience.
  • 15+ years of experience in software engineering, artificial intelligence, machine learning, or related fields, including experience serving as a Principal Engineer, AI Technical Lead, Architect, or equivalent senior technical leadership role.
  • Demonstrated success designing, building, and deploying production-scale AI and machine learning solutions.
  • Deep expertise in machine learning, generative AI, large language models, modern AI architectures, and AI engineering best practices.
  • Experience developing and deploying LLM applications, RAG systems, AI agents, and foundation model-based solutions.
  • Strong background in cloud-native architecture, distributed systems, data platforms, and AI infrastructure.
  • Experience implementing AI operationalization practices, including model deployment, monitoring, evaluation, governance, and lifecycle management.
  • Expertise in secure, responsible AI development and model governance frameworks.
  • Advanced software engineering skills and proficiency with modern programming languages, frameworks, and AI development ecosystems.
  • Exceptional communication, leadership, and collaboration skills with the ability to influence diverse technical and non-technical stakeholders.
Preferred Qualifications
  • Experience applying AI, machine learning, or advanced analytics within scientific research environments.
  • Familiarity with astronomical datasets, observatory operations, scientific instrumentation, or related research domains.
  • Experience with high-performance computing (HPC), large-scale data processing, or scientific computing platforms.
  • Contributions to open-source software, AI frameworks, or scientific computing communities.
  • Experience building organizational AI capabilities and driving enterprise-wide AI adoption strategies.

Salary Range: $179,000-$226,000. The final salary will depend on skills, qualifications, experience and job location.

This position will remain open until it is filled. Please submit a cover letter and a CV or resume, PDF files preferred. 

Individuals needing assistance with the employment process can request assistance here.

Why Join NOIRLab?
  • Lead the AI vision for one of the world's leading astronomical research organizations.
  • Build AI capabilities from the ground up with executive visibility and organizational influence.
  • Apply cutting-edge AI technologies to accelerate scientific discovery and innovation.
  • Collaborate with world-class scientists, engineers, and researchers solving some of humanity's most fundamental questions.
  • Make a lasting impact on both the future of AI and our understanding of the universe.