ZipRecruiter

Log In

1

Generative Ai Testing Jobs (NOW HIRING)

Select Minds LLC

AI Testing Architect

Dallas, TX

$120K - $135K/yr

AI Testing Architect (GenAI / QA Automation) Work Type: Full-Time/Contract Location: Dallas, Texas ... This role focuses on applying Generative AI to improve test coverage, reduce cycle time, and ...

Select Minds LLC

AI Testing Architect

Dallas, TX · On-site

$120K - $135K/yr

AI Testing Architect (GenAI / QA Automation) Work Type: Full-Time/Contract Location: Dallas, Texas ... This role focuses on applying Generative AI to improve test coverage, reduce cycle time, and ...

Envision

Generative AI Engineer

Manhattan, NY · On-site +1

No C2C No Third Party Agencies You will be responsible for designing, developing, optimizing, and testing generative AI and agentic AI solutions to enable the production of accurate, relevant, and ...

Uncountable

Platform Engineer - Generative AI

New York, NY · On-site

$120K - $160K/yr

... tuning/testing behavior) and traditional fullstack development tasks (coding frontends/backends ... Strong interest in Generative AI * Solid computer science and software engineering fundamentals ...

next page

Showing results 1-20

People also search for

All JobsGenerative Ai Testing Jobs

Generative Ai Testing information

See salary details

$31

$53

$76

How much do generative ai testing jobs pay per hour?

As of Jun 5, 2026, the average hourly pay for generative ai testing in the United States is $53.73, according to ZipRecruiter salary data. Most workers in this role earn between $44.23 and $61.54 per hour, depending on experience, location, and employer.

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.

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.

More about Generative Ai Testing jobs
What cities are hiring for Generative Ai Testing jobs? Cities with the most Generative Ai Testing job openings:
What states have the most Generative Ai Testing jobs? States with the most job openings for Generative Ai Testing jobs include:
What job categories do people searching Generative Ai Testing jobs look for? The top searched job categories for Generative Ai Testing jobs are:
Generative Ai Testing jobs near you
Infographic showing various Generative Ai Testing job openings in the United States as of May 2026, with employment types broken down into 12% Internship, 76% Full Time, 6% Part Time, and 6% Contract. Highlights an 53% In-person, 12% Hybrid, and 35% Remote job distribution, with an average salary of $111,750 per year, or $53.7 per hour.

AI Testing Architect

Select Minds LLC

Dallas, TX

$120K - $135K/yr

Full-time

Medical

Posted 11 days ago

Job description

Benefits:
  • Competitive salary
  • Health insurance
  • Opportunity for advancement

Job Title: AI Testing Architect (GenAI / QA Automation)
Work Type: Full-Time/Contract
Location: Dallas, Texas Onsite
Interview Mode: Virtual + In-Person (depends)
Work Auth : Must be authorized to work in the U.S.
Domain: Enterprise AI / Agentic AI / AWS Bedrock
Compensation: Competitive, commensurate with experience
We are hiring a senior AI Testing Architect to design and implement AI-driven solutions across software testing and quality engineering. This role focuses on applying Generative AI to improve test coverage, reduce cycle time, and modernize QA practices.
You will work hands-on with engineering and QA teams while also guiding tooling decisions and adoption approaches. This is a high-impact individual contributor role with ownership of architecture, implementation, and practical AI adoption across testing workflows.
Key Responsibilities
* Design and implement AI-driven solutions for test automation, test data generation, and defect detection
* Build and deploy LLM-based workflows (e.g., test case generation, RAG-based validation, anomaly detection)
* Evaluate, select, and integrate AI tools and frameworks for QA and SDLC use cases
* Develop reusable architecture patterns for AI-enabled testing across teams
* Integrate AI solutions into CI/CD pipelines and existing engineering workflows
* Collaborate with Engineering, QA, and DevOps teams to drive practical AI adoption
* Optimize performance, cost, and reliability of AI-based solutions in production
* Provide technical guidance and hands-on support to engineers adopting AI tools
* Contribute to lightweight AI governance practices, including data handling, security, and responsible usage
Required Qualifications
* 8+ years of experience in software engineering, QA automation, or test architecture
* 3+ years of hands-on experience with AI/ML or Generative AI in production environments
* Strong experience with test automation frameworks (Selenium, Playwright, Cypress, PyTest, TestNG)
* Strong programming skills in Python
* Experience building or integrating LLM-based solutions (prompting, RAG, embeddings, vector search)
* Experience integrating solutions into CI/CD pipelines (Jenkins, GitHub Actions, Azure DevOps)
* Experience with at least one cloud platform (AWS, Azure, or GCP)
* Strong understanding of software testing principles, QA processes, and SDLC
Preferred Qualifications
* Experience with LangChain or LlamaIndex
* Experience with vector databases (Pinecone, FAISS, Weaviate)
* Exposure to MLOps practices and model lifecycle management
* Experience with AI governance, security, or compliance frameworks
* Prior experience as an AI Architect, Solution Architect, or Principal Engineer
* Experience working in enterprise-scale environments
Technical Stack
* Languages: Python (primary), Java or JavaScript (optional)
* Testing: Selenium, Playwright, Cypress, PyTest, TestNG
* AI/GenAI: OpenAI APIs, LangChain or LlamaIndex, embeddings, RAG
* Data: Vector databases (Pinecone, FAISS, Weaviate)
* Cloud: AWS, Azure, or GCP
* CI/CD: Jenkins, GitHub Actions, Azure DevOps
Success Metrics
* Reduce regression testing cycle time through AI-driven automation
* Improve test coverage and defect detection using AI-generated test assets
* Deliver reusable AI architecture patterns adopted across teams
* Drive measurable adoption of AI tools within engineering and QA workflows