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Ai Qa Jobs (NOW HIRING)

Jobgether

$6 - $65/hr

Our partner is looking for a AI QA Trainer - LLM Evaluation based in Netherlands. This role offers the opportunity to contribute directly to the evolution of advanced AI systems by improving their ...

Saransh Inc

Conversational AI QA Lead

Irving, TX · On-site

- Conversational AI QA Lead - chatbots Locations: Irving/Dallas, Texas FULL TIME Day to Day job Duties: * Develop and implement conversational AI solutions: Design, build, and deploy chatbots and ...

Introlligent Inc.

AI QA Automation

Cupertino, CA · On-site

QA Engineer focused on testing AI/ML systems and building automation frameworks to ensure model quality, reliability, and performance. Key Responsibilities * Design and implement automated tests for ...

Vizient

Senior AI Quality & Reliability Engineer

Chicago, IL · On-site

$91K - $123K/yr

Lead AI validation activities including functional testing, prompt validation, workflow testing, regression testing, release validation, runtime quality assurance, and production reliability support.

State Street

AI QA Engineer, AVP

Burlington, MA · On-site

State Street is looking for a Quality Assurance Engineer focused on AI testing to join their team responsible for advancing quality engineering for AI-enabled capabilities. The role involves ...

Pwc

AIT - AI Quality Assurance Senior Associate

Tampa, FL · On-site

$72K - $212K/yr

Industry/Sector Not Applicable Specialism Quality Engineering Management Level Senior Associate & Summary The Opportunity As an AI Quality Assurance Senior Associate, you will play a pivotal role in ...

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Ai Qa information

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How much do ai qa jobs pay per hour?

As of Jul 30, 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 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.

What are the key skills and qualifications needed to thrive as an AI QA (Artificial Intelligence Quality Assurance) specialist, and why are they important?

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.

What are AI QA professionals?

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.

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.
More about Ai Qa jobs
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What job categories do people searching Ai Qa jobs look for? The top searched job categories for Ai Qa jobs are:
Ai Qa jobs near you
Infographic showing various Ai Qa job openings in the United States as of July 2026, with employment types broken down into 67% Full Time, 22% Part Time, and 11% Contract. Highlights an 59% Physical, 3% Hybrid, and 38% Remote job distribution, with an average salary of $83,472 per year, or $40.1 per hour.

Sr. AI QA Engineer / Lead

People Force Consulting Inc

Eden Prairie, MN • On-site

$90K - $122K/yr

Other

Posted 19 days ago

Job description

Looking for a AI Quality Engineering Lead with expertise in LLMs (OpenAI, Azure OpenAI), Agentic AI, RAG pipelines, Python automation, and AI validation frameworks, responsible for driving AI quality assurance, workflow optimization, intelligent test automation, runtime reliability, and scalable enterprise AI governance across healthcare-focused AI solutions.

Roles and Responsibilities: -

Quality Engineering Architecture & Workflow Optimization Lead architecture and technical implementation of AI Quality Engineering solutions supporting AI-powered applications, LLM-enabled workflows, intelligent automation solutions, agentic systems, and enterprise AI platforms.

Design and implement scalable AI validation frameworks, AI-assisted testing approaches, runtime quality controls, reusable testing accelerators, and workflow optimization capabilities supporting enterprise AI delivery initiatives.

Support modernization of traditional Quality Engineering practices through intelligent automation, workflow orchestration, and scalable quality engineering patterns.

Develop automated validation approaches, anomaly detection processes, testing pipelines, and quality metrics supporting governance-aligned AI deployment practices.

Design and optimize human-in-the-loop validation workflows, operational review processes, and AI quality assurance controls supporting reliable and scalable AI-enabled systems.

Partner with engineering and business teams to identify operational bottlenecks, workflow optimization opportunities, automation use cases, and scalable quality engineering improvements.

Experience: -

8+ Years

Location: -

Eden Prairie, MN(Hybrid)

Duration: -

6 Months Contract with possible extension

Educational Qualifications: -

Engineering Degree BE/ME/BTech/MTech/BSc/MSc.

Technical certification in multiple technologies is desirable.

Mandatory skills

AI Validation, Runtime Assurance & Automation Support AI validation activities, including prompt testing, workflow testing, regression testing, runtime quality assurance, and production reliability support. Partner with AI Engineering, AIOps, LLMOps, Security, Governance, Clinical, and Data teams to support scalable AI Quality Engineering and workflow automation processes across enterprise AI initiatives.

Design and support runtime quality practices, including telemetry alignment, monitoring coordination, validation processes, and runtime reliability improvement efforts.

Drive adoption of AI-assisted testing approaches, intelligent automation, reusable testing accelerators, and orchestration-aware testing practices.

Support observability and runtime visibility initiatives improving reliability, traceability, and confidence across AI-enabled systems.

Collaborate with Clinical, Operational, and Engineering stakeholders to support validation of healthcare workflows, operational processes, and AI-enabled business solutions.

Technical Enablement, Delivery & Operational Support delivery coordination activities across AI Quality Engineering and workflow optimization initiatives, including implementation planning, issue tracking, operational support, and release coordination activities.

Partner with stakeholders to evaluate implementation readiness, workflow dependencies, operational risks, automation opportunities, and quality considerations for AI initiatives. Support tooling evaluations, automation frameworks, orchestration tooling, and modernization initiatives supporting AI Quality Engineering maturity.

Help establish reusable workflow automation patterns, scalable testing assets, and engineering enablement practices across delivery teams. Support adoption of modern AI Quality Engineering and workflow optimization practices across engineering and business organizations.

Leadership, Collaboration & Continuous Improvement Lead and mentor engineers, analysts, contractors, and delivery teams while fostering a collaborative, continuously learning, and engineering-focused culture.

Communicate implementation risks, workflow optimization opportunities, technical trade-offs, and operational recommendations to technical and business stakeholders. Promote engineering discipline, continuous improvement, responsible AI adoption, and operational accountability across AI Quality Engineering initiatives.

Skills

Quality LLM, OpenAI,Azure, Python RAG Pipeline, AgenticAI.

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