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Generative Ai Testing Jobs in Allen, TX (NOW HIRING)

Lares IT Solutions Inc

Generative AI Engineer

Plano, TX · On-site

Generative AI Developer Plano, TX We are seeking a seasoned Generative AI Developer with expertise ... Testing & Tools: Cucumber (BDD), SonarQube, Postman, JIRA, Maven, Gradle, Apache Spark * Operating ...

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 ...

Noblesoft Technologies

Gen AI Lead

Dallas, TX · On-site

$138K - $170K/yr

... Generative * AI, Causal Inference, Time series analysis, Forecasting, Anomaly detection, Hypothesis testing, A/B testing, Git Actions, Tableau, Power BI, ThoughtSpot, Web Scraping * Data ...

Black Box

AI Engineer Intern - USA

Plano, TX · On-site

This role offers hands-on experience building enterprise-grade Generative AI solutions across ... Support testing, debugging, deployment, and monitoring of AI services on Azure. * Document AI ...

Black Box

AI Intern - Dallas, TX

Plano, TX · On-site

This role offers hands-on experience building enterprise-grade Generative AI solutions across ... Support testing, debugging, deployment, and monitoring of AI services on Azure. * Document AI ...

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Generative Ai Testing information

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How much do generative ai testing jobs pay per hour?

As of Jul 26, 2026, the average hourly pay for generative ai testing in Allen, TX is $49.97, according to ZipRecruiter salary data. Most workers in this role earn between $41.15 and $57.26 per hour, depending on experience, location, and employer.

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 software quality assurance. 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.

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 is the salary of generative AI tester?

The salary of a generative AI tester typically ranges from $70,000 to $120,000 annually, depending on experience, location, and company size. Entry-level positions may start lower, while experienced testers with specialized skills in AI and machine learning can earn higher salaries. Certifications in AI or related fields can also influence compensation.

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.

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 tools, testing frameworks, and quality assurance processes is also important. Gaining relevant certifications or training in AI and software testing can enhance your qualifications.

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 job categories do people searching Generative Ai Testing jobs in Allen, TX look for? The top searched job categories for Generative Ai Testing jobs in Allen, TX are:
What cities near Allen, TX are hiring for Generative Ai Testing jobs? Cities near Allen, TX with the most Generative Ai Testing job openings:
Generative Ai Testing jobs near you
Infographic showing various Generative Ai Testing job openings in Allen, TX as of July 2026, with employment types broken down into 75% Full Time, 22% Part Time, and 3% Contract. Highlights an 71% Physical, 3% Hybrid, and 26% Remote job distribution, with an average salary of $103,947 per year, or $50 per hour.

Generative AI Engineer

Lares IT Solutions Inc

Plano, TX • On-site

Other

Posted 3 days ago

Job description

Generative AI Developer

Plano, TX

We are seeking a seasoned Generative AI Developer with expertise in AWS Bedrock and enterprise-grade application development. This role combines advanced AI/ML engineering with the ability to design, build, and deliver scalable, intelligent systems. The ideal candidate will have a proven track record in deploying generative AI solutions, architecting microservices, and integrating AI/LLM capabilities into modern enterprise applications.

Key Responsibilities

  • AI System Design & Implementation: Architect and develop scalable AI agent frameworks tailored to business needs, leveraging foundational models and AWS Bedrock.
  • Bedrock Model Expertise: Fine-tune and deploy Bedrock models (LLMs, multimodal models) for use cases such as vector-based retrieval and Retrieval-Augmented Generation (RAG).
  • Enterprise Application Development: Design, develop, and deliver multi-tier, enterprise-grade applications with a strong focus on microservices, REST APIs, and Kafka-based integrations.
  • Optimization & Reliability: Enhance model performance while balancing computational cost, ensuring deployed AI systems are robust, accurate, and maintainable.
  • Innovation & Integration: Actively integrate AI/ML capabilities and LLM-based tooling into modern software systems, staying current on emerging technologies.
  • Collaboration: Work closely with data scientists, engineers, product managers, and stakeholders to translate AI research into production-ready systems.
  • Leadership & Best Practices: Mentor teams, drive engineering excellence through TDD, BDD, and DoD, and ensure delivery of high-quality software solutions.
  • Data & Infrastructure: Manage large datasets, model lifecycle tools (MLflow, Kubeflow), and containerization (Docker, Kubernetes) for reproducible, scalable environments.

Required Skills & Experience

Technical Skills

  • AI/ML & LLM: AWS Bedrock, OpenAI GPT-4 API, LangChain, Hugging Face Transformers, RAG pipelines, LLM orchestration, prompt engineering, AI-assisted coding tools (GitHub Copilot)
  • Languages: Python (scripting & AI automation), Java (working knowledge)
  • Frameworks: Spring Boot, Spring AI, Microservices, Apache Camel, Fuse ESB
  • Messaging: Apache Kafka, ActiveMQ Artemis, event-driven architecture
  • Frontend: React, HTML5, CSS3, AJAX, jQuery
  • Cloud & DevOps: AWS (EC2, S3, Lambda, SageMaker), Docker, Kubernetes, Jenkins, Ansible, Git
  • Databases: Oracle (9i/10g/12c), vector databases (Pinecone, others)
  • Testing & Tools: Cucumber (BDD), SonarQube, Postman, JIRA, Maven, Gradle, Apache Spark
  • Operating Systems: Windows, UNIX, IBM AIX

Experience: Several years of experience in AI/ML engineering, with cloud-native, generative AI integrations and familiarity with enterprise application development practices.

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