ZipRecruiter

Log In

1

Ai Qa Jobs (NOW HIRING)

LTM

Sr. AI QA Engineer

Irving, TX · On-site

Job Title: Sr. AI QA Engineer Work Location: Irving, TX Required Skills: * 7+ years of Python experience. * Hands-on experience with generative AI models and frameworks (e.g., Google ADK, OpenAI ...

TekShapers

AI Quality Engineer (AI QE)

Edina, MN · On-site

$74K - $96K/yr

... years in QA/QE, software testing, or technology delivery • 2+ years of technical leadership in testing/automation • Strong experience with test automation, SDLC, Agile, APIs, and release ...

System One

AI QA Analyst - 100% REMOTE

Washington, DC · Remote

AI QA Analyst Location: 100% Remote Type: Long-Term Contract Compensation: Negotiable, W-2 or C2C Work Model: Remote - offsite Hours: 40 hours a week Security Clearance: No clearance but must pass ...

System One

AI QA Analyst - 100% REMOTE

Washington, DC · Remote

AI QA Analyst Location: 100% Remote Type: Long-Term Contract Compensation: Negotiable, W-2 or C2C Work Model: Remote - offsite Hours: 40 hours a week Security Clearance: No clearance but must pass ...

System One

AI QA Analyst - 100% REMOTE

Washington, DC · Remote

AI QA Analyst Location: 100% Remote Type: Long-Term Contract Compensation: Negotiable, W-2 or C2C Work Model: Remote - offsite Hours: 40 hours a week Security Clearance: No clearance but must pass ...

Imprint

Introduces an AI QA layer that flags issues early (before human review), and owns the final human QA process and quality bar. This is a systems-and-quality role: you'll improve and maintain our ...

previous page

Showing results 41-60

All JobsTechnology JobsSoftware JobsAi QA Jobs

Ai Qa information

See salary details

$11

$40

$71

How much do ai qa jobs pay per hour?

As of Sep 11, 2026, the average hourly pay for ai qa in the United States is $40.13, according to ZipRecruiter salary data. Most workers in this role earn between $28.12 and $49.28 per hour, depending on experience, location, and employer.

What is an AI QA?

AI QA (Artificial Intelligence Quality Assurance) professionals are specialists who test and validate machine learning models, AI systems, and related software to ensure they function correctly, reliably, and ethically. They design test cases, evaluate model accuracy, check for biases, and verify compliance with regulatory standards. AI QA experts work closely with data scientists, developers, and product managers to identify issues and improve system performance throughout the AI development lifecycle.

What are the key skills and qualifications needed to thrive as an AI QA?

To thrive as an AI QA specialist, you need strong analytical skills, knowledge of software testing methodologies, and a solid understanding of AI/ML concepts, typically supported by a degree in computer science or a related field. Familiarity with testing frameworks like Selenium or PyTest, version control systems like Git, and tools such as TensorFlow or PyTorch for model validation is often required. Attention to detail, problem-solving abilities, and effective communication set top performers apart in this role. These skills ensure the reliability, fairness, and safety of AI systems, which is critical for delivering trustworthy and high-quality AI solutions.

How does an AI QA engineer typically collaborate with data scientists and developers during the model development lifecycle?

AI QA engineers work closely with data scientists and developers throughout the AI model development process. They often participate in sprint planning and daily stand-up meetings to align on testing strategies, clarify requirements, and address potential model issues early. Their responsibilities include designing test cases for model accuracy, fairness, and robustness, as well as validating data pipelines and deployment workflows. Effective communication and a proactive approach are essential, as AI QA engineers help ensure the final model meets quality standards and integrates smoothly into production environments.

What is the difference between Ai Qa vs Data Analyst?

AspectAi QaData Analyst
Required CredentialsTypically certifications in AI, QA, or software testingBachelor's degree in data science, statistics, or related fields
Work EnvironmentTech companies, software development teams, AI-focused projectsBusiness, finance, healthcare, and various industries analyzing data
Employer & Industry UsageTech firms, AI startups, software companiesCorporations across multiple sectors, consulting firms
Common Search & Comparison IntentUnderstanding roles in AI quality assuranceAnalyzing data to inform business decisions

Ai Qa specialists focus on testing and ensuring the quality of AI systems, often requiring knowledge of AI tools and testing protocols. Data Analysts interpret data to generate insights, requiring skills in data visualization and statistical analysis. While both roles work with data and technology, Ai Qa emphasizes quality assurance in AI development, whereas Data Analysts focus on data interpretation for decision-making.

How to become an AI QA tester?

To become an AI QA tester, you should have a strong understanding of software testing principles, familiarity with AI and machine learning concepts, and experience with testing tools and scripting languages. Gaining knowledge of AI models, data validation, and quality assurance processes is essential, and certifications in software testing or AI can enhance your qualifications.
More about Ai Qa jobs

What cities are hiring for Ai Qa jobs?

Cities with the most Ai Qa job openings:

What are the most commonly searched types of Ai Qa jobs?

The most popular types of Ai Qa jobs are:

What states have the most Ai Qa jobs?

States with the most job openings for Ai Qa jobs include:

What are popular job titles related to Ai Qa jobs?

For Ai Qa jobs, the most frequently searched job titles are:

Ai Qa jobs near you
Infographic showing various Ai Qa job openings in the United States as of September 2026, with employment types broken down into 56% Full Time, 22% Part Time, and 22% Contract. Highlights an 78% In-person, and 22% Remote job distribution, with an average salary of $83,472 per year, or $40.1 per hour.

Manager - Quality Assurance (IT)

Las Vegas, NV • On-site

Wynn Las Vegas
Hospitality Services • 10K+ employees
Learn more about Wynn Las Vegasexternal

Full-time

This job post has expired today. Applications are no longer accepted.

Job description

Job Description

The Manager of Quality Engineering and Automation is a hands-on quality engineering leader responsible for helping transform a traditional Quality Assurance organization into a modern Quality Engineering organization. This role embeds quality throughout the SDLC by advancing automation-first practices, AI-assisted testing, continuous testing, and measurable engineering standards that improve delivery speed, reliability, and guest-facing quality.

This leader will drive adoption of AI-assisted Quality Engineering, including agentic agents capable of reading user stories, acceptance criteria, code changes, build outputs, deployments, and release plans to generate, execute, and maintain automated tests across smoke, functional, regression, user experience, integration, performance, load, and other testing needs.

The role manages quality engineers and automation resources across agile product teams and structured project delivery efforts, ensuring consistent standards, clear reporting, and continuous improvement.
This position ensures Wynn’s digital platforms and enterprise systems meet high standards for quality, performance, security, reliability, and guest experience.

Success Profile (What “Great” Looks Like)

  • QA practices evolve into a modern Quality Engineering organization with stronger automation, earlier testing, and measurable quality outcomes
  • AI Quality Engineering assistants are used to generate, execute, maintain, and report on automated tests using stories, acceptance criteria, code, builds, deployments, and release plans
  • Nightly automation reports provide clear visibility into test results, quality trends, coverage gaps, performance/load concerns, and release risks
  • Manual testing effort is reduced while improving automation coverage, test reliability, and delivery confidence
  • Digital platforms and enterprise systems consistently meet Wynn’s premium standards for quality, reliability, performance, and guest experience

Key Responsibilities

Quality Engineering Transformation, Automation & AI Enablement

  • Lead the transition from traditional QA practices to a modern Quality Engineering model focused on prevention, automation, engineering discipline, and continuous feedback
  • Implement automation-first testing practices across smoke, functional, API, regression, user experience, integration, performance, load, and release validation activities
  • Drive adoption of AI Quality Engineering assistants and agentic testing agents to accelerate test creation, execution, maintenance, and reporting
  • Establish practical standards, frameworks, and governance for AI-assisted test automation and quality engineering practices
  • Champion shift-left quality, continuous testing, and early defect detection across delivery teams

Delivery Model Leadership

  • Manage quality engineering delivery across agile product teams for testing efforts
  • Align testing strategy to product risk, system complexity, release cadence, and business criticality
  • Partner with engineering, product, DevOps, infrastructure, data, and business teams to ensure testing is planned early and executed consistently
  • Ensure appropriate quality gates, release readiness criteria, and reporting are in place for both agile and project-based delivery models

AI Quality Engineering Assistants & Agentic Test Automation

  • Help design and operationalize AI Quality Engineering assistants that can interpret multiple engineering inputs, including user stories, acceptance criteria, actual code, build artifacts, deployment schedules, and release plans
  • Enable agentic agents to generate, execute, monitor, and maintain automated test suites across smoke, functional, API, regression, user experience, integration, performance, load, and release validation testing
  • Establish scheduled automated test execution and reporting capabilities that summarize pass/fail results, defect trends, coverage gaps, performance concerns, and release risks
  • Implement self-healing and intelligent automation patterns that reduce maintenance effort and improve test reliability
  • Ensure AI-generated tests, recommendations, and reports are explainable, auditable, and validated through appropriate engineering controls

Engineering & Delivery Excellence

  • Own quality outcomes across the SDLC, including functional quality, integration reliability, performance, load readiness, security validation, accessibility, and guest experience
  • Embed automated testing into CI/CD pipelines and release workflows to provide fast, reliable quality feedback
  • Partner with engineering teams to improve testability, observability, code quality, and defect prevention
  • Support release readiness through data-driven quality insights, risk assessments, and clear go/no-go recommendations

Metrics & Continuous Improvement

  • Define and track quality engineering metrics, including automation coverage, defect escape rate, test stability, execution duration, performance trends, load test results, and AI-assisted testing effectiveness
  • Produce clear recurring reporting, including automation summaries, release readiness dashboards, and quality risk insights
  • Use data, AI-generated insights, and team retrospectives to continuously improve testing practices, tooling, coverage, and speed

Team Leadership & Capability Building

  • Manage, coach, and develop quality engineers and automation engineers supporting digital platforms
  • Build team capability in AI-assisted testing, agentic automation, CI/CD testing, performance testing, load testing, test data management, and engineering-led quality practices
  • Create a culture of ownership, curiosity, technical excellence, and continuous improvement
  • Partner with leaders across engineering, product, operations, and enterprise systems to align quality priorities with business outcomes

Governance, Risk & Compliance

  • Establish governance for automated testing, AI-assisted testing, test data, quality gates, release readiness, and production risk assessment
  • Ensure AI-generated outputs, test results, and recommendations are traceable to requirements, acceptance criteria, code changes, and release scope
  • Partner with security, compliance, and engineering teams to ensure quality practices support auditability, privacy, and regulatory expectations
  • Maintain appropriate controls for human review, exception handling, and approval of AI-assisted quality decisions
Qualifications
  • A minimum of seven (7) years of experience in software quality, test automation, quality engineering, or software engineering, with two (2) years leading or managing teams
  • Demonstrated experience transforming QA practices toward quality engineering, automation-first testing, and shift-left quality
  • Hands-on experience with test automation frameworks, CI/CD integration, API testing, regression testing, and release validation
  • Experience with performance, load, scalability, and reliability testing practices
  • Practical experience applying AI, LLMs, or intelligent automation to testing, quality analysis, test generation, defect detection, or reporting
  • Strong understanding of agile delivery, user stories, acceptance criteria, build pipelines, deployment processes, and release management
  • Ability to translate technical quality insights into clear business and release risk communication

Preferred Experience

  • Experience implementing AI-assisted or agentic testing capabilities in an enterprise environment
  • Experience with cloud-based platforms, DevOps toolchains, Azure DevOps, GitHub, CI/CD pipelines, observability tools, and automated reporting
  • Hospitality, gaming, or high-touch customer experience environments
  • Guest-facing platforms such as web, mobile, contact center, booking, loyalty, or digital service experiences
  • Enterprise systems such as PMS, CRS, CRM, contact center, workforce management, or integration platforms
  • 24/7, high-availability, revenue-impacting environments

Additional Information

Wynn Resorts is an equal opportunity employer committed to hiring a diverse workforce and sustaining an inclusive culture. Wynn Resorts does not discriminate on the basis of disability, veteran status or any other basis protected under federal, state or local laws confidential according to EEO guidelines.