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Generative Ai Testing Jobs in Ohio (NOW HIRING)

ArtiFlex Manufacturing

AI Co-Op

Wooster, OH

Build and optimize machine learning and Generative AI solutions, including RAG (Retrieval-Augmented ... Participate in code reviews, debugging, testing, and performance optimization * Communicate clearly ...

ArtiFlex Manufacturing

AI Co-Op

Wooster, OH · On-site

Build and optimize machine learning and Generative AI solutions, including RAG (Retrieval-Augmented ... Participate in code reviews, debugging, testing, and performance optimization * Communicate clearly ...

The Provato Group

Senior AI / ML Engineer

Cleveland, OH · On-site

$101K - $139K/yr

You will play a key role in building scalable ML and generative AI capabilities, guiding ... and automated testing frameworks. * Create evaluation strategies that combine offline metrics ...

Diverse Lynx

Ai/Ml Architect

Marysville, OH

$58.50 - $75.25/hr

Strong understanding of software engineering principles, design patterns, and testing practices ... Experience with generative AI, LLM fine tuning, and RAG architectures. * Background in industry ...

VDart

Senior AI / ML Engineer

Mayfield Village, OH

$107K - $146K/yr

Expert in Python and SQL; strong software engineering practices (testing, patterns, performance ... PyTorch or TensorFlow, transformers, CV/NLP pipelines. • Generative AI: LLMs, RAG, fine-tuning ...

Purple Drive

Lead AI-ML Engineer

Westerville, OH

$99K - $130K/yr

... testing, regression analysis, and variance modeling. • Experience with anomaly detection techniques - supervised, unsupervised, and hybrid approaches. • Experience in Generative AI based ...

Purple Drive Technologies

Lead AI-ML Engineer

Westerville, OH · On-site

$98K - $130K/yr

... testing, regression analysis, and variance modeling. • Experience with anomaly detection techniques - supervised, unsupervised, and hybrid approaches. • Experience in Generative AI based ...

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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.

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 are popular job titles related to Generative Ai Testing jobs in Ohio? For Generative Ai Testing jobs in Ohio, the most frequently searched job titles are:
What job categories do people searching Generative Ai Testing jobs in Ohio look for? The top searched job categories for Generative Ai Testing jobs in Ohio are:
What cities in Ohio are hiring for Generative Ai Testing jobs? Cities in Ohio with the most Generative Ai Testing job openings:
Generative Ai Testing jobs near you
Infographic showing various Generative Ai Testing job openings in Ohio as of June 2026, with employment types broken down into 10% Internship, 72% Full Time, 13% Part Time, and 5% Temporary. Highlights an 80% In-person, 5% Hybrid, and 15% Remote job distribution.
AI Co-Op

AI Co-Op

ArtiFlex Manufacturing

Wooster, OH

Full-time

Posted 15 days ago

ArtiFlex Manufacturing rating

7.9

Company rating: 7.9 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

Job description

AI Co-Op
General Description:
Artiflex Manufacturing is seeking a highly motivated Technology Intern with a strong foundation in AI, data engineering, and modern software development to support real-world manufacturing, quality, and operations use cases. This role offers hands-on exposure to building, deploying, and scaling AI-driven applications using enterprise data.
The intern will work closely with engineering and business stakeholders to design, develop, and deploy data- and AI-powered solutions that improve operational efficiency, quality analytics, and decision-making across the organization.
Responsibilities:
  • Design, develop, and maintain AI and data-driven applications for manufacturing, quality, and operational analytics
  • Work with large-scale datasets stored in SQL Server and cloud data platforms
  • Build and optimize machine learning and Generative AI solutions, including RAG (Retrieval-Augmented Generation) systems
  • Develop and deploy AI models and AI-powered applications into production environments
  • Collaborate on data pipelines, ETL/ELT workflows, and analytics solutions (Databricks preferred)
  • Contribute innovative ideas to solve complex, real-world manufacturing problems
  • Follow Artiflex Code of Behaviors, engineering standards, and best practices
  • Participate in code reviews, debugging, testing, and performance optimization
  • Communicate clearly with technical and non-technical stakeholders
Required Qualifications
  • Prior internship experience or minimum 1 year of relevant work experience
  • Currently pursuing or recently completed a Master’s degree in Computer Science, Data Science, Information Technology, or Artificial Intelligence / Machine Learning (preferred)
  • Strong programming skills in Python, SQL, and C / C++
  • Experience working with large datasets and relational databases (SQL Server preferred)
  • Strong understanding of modern AI trends, especially Generative AI
  • Proven ability to think critically, solve problems quickly, and work independently
  • Excellent communication and collaboration skills
  • Familiarity with modern cloud-based data and AI platforms is preferred but not mandatory, such as:
  • Lakehouse platforms (Databricks or Snowflake)
  • AWS (S3, Glue, Athena, Redshift, SageMaker)
  • Google Cloud Platform (BigQuery, Vertex AI, Cloud Storage)
  • Azure (preferred) (Azure Data Lake, Synapse, Azure ML)
Preferred Qualifications
  • Hands-on experience developing AI/ML models, applications, and deploying them to production
  • Strong experience with RAG systems, vector databases, and embedding-based search
  • Experience building, fine-tuning, or evaluating LLMs
  • Hands-on experience with Generative AI frameworks (LangChain, LlamaIndex, OpenAI, etc.)
  • Experience using CLI tools, Git workflows, and CI/CD pipelines
  • Experience working on large, collaborative development teams
  • Exposure to cutting-edge web technologies and AI-enabled applications
  • Experience with UX/UI principles, design-centric approaches, and building engaging user interfaces
What You’ll Gain
  • Real-world experience applying AI and data engineering in manufacturing and enterprise environments
  • Hands-on exposure to production-grade AI systems
  • Opportunity to contribute directly to AI initiatives that impact operations and decision-making

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