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Generative Ai Testing Jobs in Texas (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 ...

Prosum Inc.

Generative AI Engineer

Fort Worth, TX · On-site

$120K - $170K/yr

Generative AI Engineer Location: Remote (U.S.) Salary Range: $120k to $170k About the Role We are ... Develop evaluation pipelines for LLM outputs, including regression testing and failure analysis

Droisys

Generative AI LangGraph experience

Plano, TX · On-site

Generative AI Engineer with LangGraph experience Plano, TX- Fully Onsite from Day-1 Core Technical ... Expertise in designing, testing, and optimizing prompts for generative models to achieve desired ...

Infosys

AI/ML Engineer with Generative AI

Plano, TX · On-site

... code, testing techniques, and support activities to enrich the knowledge base and assist other ... and generative AI. • Experience in Optimizing and adapting prompt engineering strategies to ...

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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 much do AI testers get paid?

AI testers, involved in evaluating and validating generative AI models, typically earn salaries ranging from $60,000 to $120,000 annually depending on experience, location, and company size. Entry-level positions may start lower, while experienced testers with specialized skills in machine learning and data analysis can earn higher wages.

Is AI testing a good career?

AI testing, including roles like Generative AI Testing, is a growing field with increasing demand for skills in machine learning, data analysis, and programming. It offers opportunities in tech companies, research labs, and startups, often requiring knowledge of AI frameworks and testing tools. The career can be stable and rewarding for those with technical expertise and an interest in AI development and quality assurance.

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 a $900,000 AI job?

A $900,000 AI job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers, AI research directors, or executive positions, often requiring advanced skills, extensive experience, and sometimes specialized certifications. These roles usually involve leading AI development projects, strategic planning, and overseeing AI teams in large organizations or tech companies.

How do I become an AI tester?

To become an AI tester, you should have a strong understanding of machine learning concepts, programming skills in languages like Python, and experience with data annotation and model evaluation. Familiarity with AI development tools and testing frameworks, along with attention to detail, is essential for identifying issues in AI systems.

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 are popular job titles related to Generative Ai Testing jobs in Texas? For Generative Ai Testing jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Generative Ai Testing jobs in Texas look for? The top searched job categories for Generative Ai Testing jobs in Texas are:
What cities in Texas are hiring for Generative Ai Testing jobs? Cities in Texas with the most Generative Ai Testing job openings:
Generative Ai Testing jobs near you
Infographic showing various Generative Ai Testing job openings in Texas as of June 2026, with employment types broken down into 10% Internship, 71% Full Time, 13% Part Time, and 6% Temporary. Highlights an 80% In-person, 5% Hybrid, and 15% Remote job distribution.

AI Testing Architect

Select Minds LLC

Dallas, TX

$120K - $135K/yr

Full-time

Medical

Posted 18 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