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Ai Qa Jobs (NOW HIRING)

System One

AI QA ANALYST - 100% REMOTE

San Diego, CA · 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 ...

Finite State

In our environment, AI is a first-class partner in software creation. The QA Staff/Lead Engineering Manager will define how quality is engineered into the product using automation, AI agents ...

BRMi

AI Quality Engineer

Vienna, VA · On-site

$72K - $93K/yr

This is not a traditional QA role. The AI Quality Engineer will establish quality standards, validate AI solutions, develop testing frameworks, and certify AI applications before release. The role ...

Deposco

AI Evaluation Engineer

Alpharetta, GA · On-site

Deposco is seeking an experienced AI Evaluation Engineer to join our innovative quality assurance team. This role focuses on ensuring the accuracy, reliability, and performance of AI-driven ...

Caraway

QA Manager, Customer Experience

Manhattan, NY · On-site +1

$95K - $105K/yr

This role sits at the intersection of CX strategy, data, training, AI, and operations, and reports ... If you believe QA should prevent fires, not just document them, you'll feel very at home here.

e2f, inc.

Linguistics, translation, or localization QA. * Content moderation / Trust & Safety. * Experience in AI data annotation, evaluation, or QA. What Success Looks Like: * High consistency in quality ...

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How much do ai qa jobs pay per hour?

As of Aug 14, 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?

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 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.

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.
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Ai Qa jobs near you
Infographic showing various Ai Qa job openings in the United States as of August 2026, with employment types broken down into 67% Full Time, and 33% Part Time. Highlights an 67% In-person, and 33% Remote job distribution, with an average salary of $83,472 per year, or $40.1 per hour.

AI QA Analyst - 100% REMOTE

System One Holdings, LLC

Washington, DC • On-site, Remote

$60 - $80/hr

Full-time

Medical, Dental, Vision, Life, Retirement

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

Job description

Job Title: 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 background check which will include credit check
Ideal Candidate Profile
A highly technical analyst who understands both modern software engineering and AI systems. This individual can independently assess whether an AI solution is accurate, secure, explainable, compliant, observable, and ready for production. They serve as the organization's independent quality authority for AI-powered solutions and help establish confidence in enterprise AI deployments before release to production.
Responsibilities
• Establish and execute quality, validation, certification, and production readiness processes for AI-powered SDLC and enterprise automation solutions
• Develop and implement evaluation frameworks measuring accuracy, relevance, groundedness, completeness, hallucination rates, retrieval quality, recommendation quality, and user satisfaction
• Validate Retrieval-Augmented Generation (RAG) solutions utilizing Azure AI Search, Azure AI Foundry, LangChain, LangGraph, and enterprise repositories
• Perform architecture reviews and quality assessments for AI solutions deployed on Azure services such as Containers, Functions, Databricks, SQL, Cosmos DB
• Evaluate AI orchestration workflows including prompt execution, agent routing, tool calling, guardrails, retrieval pipelines, and human-in-the-loop processes
• Validate security, governance, auditability, and compliance controls, including integrations with Entra ID, RBAC, managed identities, Key Vault, and data policies
• Create production readiness checklists covering observability, monitoring, resiliency, recoverability, and operational support
• Analyze traces, logs, metrics, and telemetry data to identify quality concerns and improvement opportunities
• Collaborate with development teams for remediation of quality, security, and performance issues before deployment
• Generate certification reports, scorecards, dashboards, and executive summaries for leadership review
• Drive continuous improvements to validation methodologies, testing strategies, and evaluation frameworks
• Lead certification and production readiness reviews for internal developer portals and platform initiatives
Requirements
• Bachelor's Degree in Computer Science, Software Engineering, Data Science, AI, or related field
• 5+ years of experience in software quality engineering, platform engineering, software development, or related roles
• 2+ years working directly with Generative AI, LLMs, RAG architectures, AI agents, or AI applications
• Strong understanding of AI evaluation methodologies including accuracy testing and hallucination detection
• Experience with Azure AI Foundry, Azure OpenAI, LangChain, LangGraph, LangSmith, or similar frameworks
• Familiarity with cloud-native architectures and Azure services (Container Apps, Functions, Databricks, SQL, Key Vault, Cosmos DB)
• Experience supporting enterprise developer platforms and self-service engineering capabilities
• Skilled in designing automated test strategies, regression testing, and certification processes
• Strong analytical, troubleshooting, and documentation skills
• Excellent stakeholder engagement and communication abilities
Preferred Qualifications
• Experience building or validating enterprise AI agents and multi-agent systems
• Knowledge of AI observability and evaluation platforms like LangSmith
• Familiarity with knowledge retrieval solutions, embeddings, and semantic search
• Background in AI governance, Responsible AI, and compliance frameworks
• Experience working in regulated industries such as healthcare, finance, or insurance
• Proficiency with Azure DevOps, GitHub, and SDLC tooling
• Knowledge of identity management concepts including Entra ID and RBAC
• Ability to produce quality scorecards, dashboards, and KPI reports
System One, and its subsidiaries including Joulé and Mountain Ltd., are leaders in delivering outsourced services and workforce solutions across North America. We help clients get work done more efficiently and economically, without compromising quality. System One not only serves as a valued partner for our clients, but we offer eligible employees health and welfare benefits coverage options including medical, dental, vision, spending accounts, life insurance, voluntary plans, as well as participation in a 401(k) plan.
System One is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, age, national origin, disability, family care or medical leave status, genetic information, veteran status, marital status, or any other characteristic protected by applicable federal, state, or local law.
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