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Generative Ai Testing Jobs in Arizona (NOW HIRING)

Pwc

US Tech - AI Engineering Senior Associate

Phoenix, AZ · On-site

$55K - $187K/yr

... and validate Generative AI agents and data pipelines, promoting reliability, scalability, and ... Responsibilities - Apply automated testing and governance controls effectively - Analyze complex ...

Sonatafy Technology

Principal Engineer

Phoenix, AZ · On-site +1

... Generative AI Development Toolchains, LLMs, and automation-driven engineering practices. * Establish technical standards for code quality, CI/CD automation, testing, and documentation. * Partner with ...

Sonatafy Technology

Principal Engineer

Phoenix, AZ · On-site

... Generative AI Development Toolchains, LLMs, and automation-driven engineering practices. * Establish technical standards for code quality, CI/CD automation, testing, and documentation. * Partner with ...

Sonatafy Technology Inc

Principal Engineer

Phoenix, AZ · On-site

... Generative AI Development Toolchains, LLMs, and automation-driven engineering practices. * Establish technical standards for code quality, CI/CD automation, testing, and documentation. * Partner with ...

Sonatafy Technology

Principal Engineer

Phoenix, AZ · On-site

... Generative AI Development Toolchains, LLMs, and automation-driven engineering practices. * Establish technical standards for code quality, CI/CD automation, testing, and documentation. * Partner with ...

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Generative Ai Testing information

What is the difference between Generative Ai Testing vs Data Scientist?

AspectGenerative Ai TestingData Scientist
Required CredentialsKnowledge of AI models, testing tools, programming skillsStatistics, programming, data analysis certifications
Work EnvironmentAI development teams, testing labs, tech companiesResearch labs, tech firms, finance, healthcare
Employer & Industry UsageAI product testing, quality assurance in techData analysis, predictive modeling across industries

Generative Ai Testing focuses on evaluating and validating AI-generated content and models, ensuring quality and accuracy. Data Scientists analyze data, build models, and derive insights. While both roles require programming and AI knowledge, Generative Ai Testing emphasizes testing processes, whereas Data Scientists focus on data analysis and model development.

How do I become a Generative AI Testing?

To become a Generative AI Tester, develop skills in machine learning, natural language processing, and programming languages like Python. Gain experience with AI frameworks such as TensorFlow or PyTorch and understand data quality and model evaluation techniques. Certifications in AI or data science can enhance your qualifications and improve job prospects.

Is Generative AI Testing a good career?

Generative AI Testing is a growing field within AI development that involves evaluating the quality and safety of AI-generated content. It requires skills in machine learning, programming, and understanding AI models, making it a promising career path with increasing demand as AI technologies expand. Professionals in this area can find opportunities in tech companies, research labs, and startups focused on AI innovation.

What are the key skills and qualifications needed to thrive as a generative AI testing specialist, and why are they important?

To thrive as a Generative AI Testing Specialist, you need a robust understanding of machine learning principles, model evaluation techniques, and a background in computer science or a related field. Familiarity with tools such as Python, TensorFlow, PyTorch, and model evaluation frameworks, as well as experience with automated testing platforms, is typically required. Analytical thinking, attention to detail, and strong communication skills help you identify model weaknesses and collaborate effectively with development teams. These skills are crucial to ensure the reliability, safety, and ethical deployment of generative AI solutions.

What are some common challenges faced when testing generative AI models, and how can I prepare to address them in this role?

Testing generative AI models often involves unique challenges such as evaluating the quality and relevance of generated content, detecting bias or inappropriate outputs, and ensuring model consistency across various prompts. You may work closely with data scientists and engineers to create robust evaluation frameworks and develop automated as well as manual testing strategies. Familiarity with prompt engineering, statistical evaluation techniques, and domain-specific knowledge will help you address these challenges effectively. Proactively staying updated on industry best practices and collaborating with cross-functional teams are key to success in this dynamic field.

What is generative AI testing?

Generative AI Testing refers to the process of evaluating and validating AI systems, particularly those that generate content such as text, images, or code. This type of testing focuses on assessing the accuracy, reliability, fairness, and safety of generative models to ensure they function as intended and avoid producing harmful or biased outputs. Testers use various methods, including automated and manual techniques, to check for issues like hallucinations, inappropriate content, or security vulnerabilities. The goal is to build trust in generative AI systems and ensure they meet quality and ethical standards before deployment.
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Generative Ai Testing jobs near you
Infographic showing various Generative Ai Testing job openings in Arizona as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 9% Part Time, 3% Contract, and 1% Nights. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution.

Fullstack GenAI Engineer/Sr. S/W Engineer

Tanisha Systems

Phoenix, AZ • On-site

Other

Posted 24 days ago

Job description

Full stack GenAI Engineer/Sr. S/W Engineer Location: Phoenix, AZ - Hybrid Full time/contract Start: Immediate Top 3 Must Have Skills: Top Requirements: Angular Node Mongodb Nice to have: Generative AI, LLMs, prompt engineering, and RAG concepts. AI-assisted software development, testing, documentation, and code review. What you'll do • Developer working on maintenance product applications. • Collaborates with leaders, business analysts, project managers, IT architects, technical leads and other developers, along with internal customers, to understand requirements and develop needs according to business requirements • Maintains and enhances existing enterprise services, applications, and platforms using domain driven design and test-driven development • Troubleshoots and debugs complex issues; identifies and implements solutions • Creates detailed project specifications, requirements, and estimates • Researches and implements new technologies to enhance current processes, security, and performance • Supports the development of coding standards and adheres to best practices and security guidelines • Works closely with software architects and technical leads to ensure decisions meet long-term enterprise growth needs All you'll need for success Minimum Qualifications- Education & Prior Job Experience • Bachelor's degree in Computer Science, Computer Engineering, Technology, Information Systems (CIS/MIS), Engineering or related technical discipline, or equivalent experience/training • 7+ years of full Software Development Life Cycle (SDLC) experience designing, developing, implementing, Monitoring and Support largescale applications in hosted production environments on prem and in the cloud

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