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Generative Ai Testing Jobs (NOW HIRING)

Prophecy Technologies

Generative AI Engineer

Schaumburg, IL · On-site

$95K - $131K/yr

The ideal candidate will possess a strong background in both Generative AI and traditional Machine ... Familiarity with functional and non-functional testing of AI/ML applications and operationalizing ...

Wipro Limited

AI Architect - Generative AI

Irving, NY · On-site

$80K - $158K/yr

AI Architect - Generative AI City: Irving State/Province: Texas Posting Start Date: 7/31/26 Wipro ... build, testing, and production rollout. • Translate complex AI concepts into clear ...

Socket.dev

Generative AI Engineer

Lewisville, TX · On-site

$140 - $190/hr

Position Description We are seeking a Generative AI Engineer to own the hands-on technical delivery ... Build and maintain automated evaluation pipelines for LLM outputs - prompt regression testing ...

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 ...

Introlligent Inc.

QA

Austin, TX · On-site

$41 - $55.75/hr

Develop AI testing and evaluation pipelines using Python. * Collaborate with AI/ML teams to improve AI application quality. Required Skills * Strong experience in AI Engineering , Generative AI , and ...

Introlligent Inc.

QA

Cupertino, CA · On-site

$51 - $69.50/hr

Develop AI testing and evaluation pipelines using Python. * Collaborate with AI/ML teams to improve AI application quality. Required Skills * Strong experience in AI Engineering , Generative AI , and ...

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How much do generative ai testing jobs pay per hour?

As of Aug 10, 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 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 do I become a Generative AI Testing?

To become a Generative AI Tester, develop skills in machine learning, natural language processing, and programming languages like Python. Gain experience with AI frameworks such as TensorFlow or PyTorch and understand data quality and model evaluation techniques. Certifications in AI or data science can enhance your qualifications and improve job prospects.

Is Generative AI Testing a good career?

Generative AI Testing is a growing field within AI development that involves evaluating the quality and safety of AI-generated content. It requires skills in machine learning, programming, and understanding AI models, making it a promising career path with increasing demand as AI technologies expand. Professionals in this area can find opportunities in tech companies, research labs, and startups focused on AI innovation.

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.
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Generative Ai Testing jobs near you
Infographic showing various Generative Ai Testing job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 9% Part Time, 4% Contract, and 1% Nights. Highlights an 89% Physical, 2% Hybrid, and 9% Remote job distribution, with an average salary of $111,750 per year, or $53.7 per hour.

Generative AI Engineer

Prophecy Technologies

Schaumburg, IL • On-site

$95K - $131K/yr

Full-time

Posted 4 days ago

Job description

Role Overview:
We are seeking a highly motivated and adaptable AI Engineer to join an innovative team. The ideal candidate will possess a strong background in both Generative AI and traditional Machine Learning, with a proven ability to work on existing applications and develop new solutions. This role is crucial for migrating and enhancing AI-driven projects, leveraging Large Language Models (LLMs), and building intelligent agents in a fast-paced environment.
Key Responsibilities:
  • Develop, maintain, and enhance existing applications utilizing Generative AI and traditional Machine Learning.
  • Work extensively with Large Language Models (LLMs) to build and refine AI-powered features.
  • Design, create, and deploy intelligent agents to automate and optimize processes.
  • Collaborate with stakeholders to translate business requirements into technical specifications for AI engineering projects.
  • Conduct experiments, implement new technologies, and contribute to the rapid evolution of AI capabilities.
  • Lead the migration of existing projects, including those with agents and LLMs, to the Google Cloud Platform (GCP) environment.
  • Engage in full-stack development, focusing on Python for backend services and React for frontend interfaces.
  • Perform data analysis using SQL to query and manipulate data from MS SQL Server, MySQL, and other relational databases.

Required Skills:
  • Strong proficiency in Python for backend development and machine learning.
  • Solid experience with React for frontend development.
  • Expertise in SQL and experience working with relational databases such as MS SQL Server and MySQL.
  • Hands-on experience with GCP AI/ML services, including BigQuery, Google SQL, and other GCP technologies and APIs.
  • Demonstrated experience with concepts such as MCP, RAG, Semantic search, Generative AI, LLMs, and building agent-based systems.
  • Ability to stay up to date with new and upcoming technologies around AI development (e.g., Agentic RAG).
  • Ability to quickly grasp new requirements, experiment with new technologies, and adapt to a rapidly changing environment.
  • Experience in taking over existing AI/ML applications from other teams.
  • Ability to estimate high-level cost of usage in GCP for applications hosted/deployed.
  • Familiarity with functional and non-functional testing of AI/ML applications and operationalizing them in production.

Qualifications:
  • Bachelor's degree in Computer Science, Engineering, or a related field.
  • A minimum of 2-3 years of professional experience in an AI or Machine Learning engineering role.

Preferred Skills:
  • Familiarity with Amazon Web Services (AWS).
  • Knowledge of LLM models trending in the industry.
  • A portfolio of projects demonstrating expertise in AI and full-stack development.

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