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Generative Ai Testing Jobs in Tennessee (NOW HIRING)

CGI Inc.

AI/ML Engineer

Knoxville, TN · On-site

This role blends full stack engineering with modern AI/ML, integrating generative AI and advanced ... CI/CD with Jenkins/GitLab/Bitbucket; testing frameworks; package managers. o Cloud: Production ...

Pilot Flying J

Sr Engineer, AI

Knoxville, TN

$86K - $118K/yr

Establish MLOps best practices including automated testing and evaluation * Implement comprehensive ... Research and implement state-of-the-art generative AI models and agent architectures * Build ...

Pilot Company

Sr Engineer, AI

Knoxville, TN · On-site

$86K - $118K/yr

Establish MLOps best practices including automated testing and evaluation * Implement comprehensive ... Research and implement state-of-the-art generative AI models and agent architectures * Build ...

Pilot Company

Sr Engineer, AI

Knoxville, TN · On-site

$86K - $118K/yr

Establish MLOps best practices including automated testing and evaluation * Implement comprehensive ... Research and implement state-of-the-art generative AI models and agent architectures * Build ...

Pilot Company

Sr Engineer, AI

Knoxville, TN · On-site

$86K - $118K/yr

Establish MLOps best practices including automated testing and evaluation * Implement comprehensive ... Research and implement state-of-the-art generative AI models and agent architectures * Build ...

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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 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 are popular job titles related to Generative Ai Testing jobs in Tennessee? For Generative Ai Testing jobs in Tennessee, the most frequently searched job titles are:
What job categories do people searching Generative Ai Testing jobs in Tennessee look for? The top searched job categories for Generative Ai Testing jobs in Tennessee are:
What cities in Tennessee are hiring for Generative Ai Testing jobs? Cities in Tennessee with the most Generative Ai Testing job openings:
Generative Ai Testing jobs near you
Infographic showing various Generative Ai Testing job openings in Tennessee as of July 2026, with employment types broken down into 73% Full Time, 24% Part Time, and 3% Contract. Highlights an 71% Physical, 3% Hybrid, and 26% Remote job distribution.
Cloud Engineer- Data/AI Focused

Cloud Engineer- Data/AI Focused

Innovative Solutions

Nashville, TN • On-site

$100K - $160K/yr

Full-time

Posted 20 days ago

Job description

As a Data & AI Engineer, you'll build and deploy modern data pipelines and generative AI solutions on AWS - from scalable ETL/ELT workflows and cloud data platforms to production-grade RAG systems and AI agents. One engagement you may be designing and optimizing data pipelines using Glue, Lambda, and Redshift, the next you're building GenAI applications powered by Bedrock, Claude, and vector databases to enable intelligent search and automation. You'll work directly with clients to deliver end-to-end data and AI solutions that are secure, scalable, and production-ready.

What You'll Do:

  • Implementing data pipelines using AWS services such as Glue, Lambda, Step Functions, and EMR
  • Creating and maintaining data extraction, transformation, and loading processes
  • Configuring and optimizing AWS database services including RDS, Aurora, Redshift, and DynamoDB
  • Implementing data lakes using S3 and related AWS services
  • Designing and building production-ready Generative AI applications using Amazon Bedrock and foundation models such as Anthropic Claude
  • Building and optimizing RAG (Retrieval-Augmented Generation) pipelines with vector databases
  • Developing AI agents and multi-agent orchestration systems using frameworks like LangChain or LlamaIndex
  • Writing and testing SQL queries and stored procedures
  • Documenting technical solutions and providing knowledge transfer to customers
  • Supporting the implementation of data governance and security controls
  • Troubleshooting and resolving issues with data pipelines and AI services
  • Participating in code reviews and implementing feedback
  • Assisting with proof-of-concept implementations for customer engagements

Required Skills:

  • 5+ years of software engineering experience with at least 2+ years focused on AI/ML, data engineering, or cloud-native development
  • 2+ years of hands-on AWS experience with production deployments
  • 1+ years of direct Generative AI experience (LLMs, embeddings, RAG, agents)
  • Proven track record delivering production AI applications from concept to deployment
  • Strong understanding of software engineering best practices (version control, testing, code review, documentation)
  • Experience working in agile/scrum environments with distributed teams
  • Excellent problem-solving skills and ability to work independently with minimal supervision
  • Strong written and verbal communication skills for client-facing interactions

Preferred:

  • Technical familiarity with AWS data and AI/ML services and modern data engineering practices
  • Hands-on experience with ETL/ELT processes and data transformation
  • Exposure to Generative AI concepts including LLMs, embeddings, RAG, and agent frameworks
  • Ability to write and optimize SQL queries across various database platforms
  • Knowledge of data modeling concepts and best practices
  • Strong analytical and problem-solving skills
  • Eagerness to learn new technologies and keep up with cloud and AI innovations
  • Excellent communication skills with the ability to explain technical concepts clearly
  • Attention to detail and commitment to solution quality
  • Collaborative mindset with strong teamwork capabilities
  • Experience or interest in automation and infrastructure as code
$100,000 - $160,000 a year
The salary range provided is a general guideline. When extending an offer, Innovative considers factors including, but not limited to, the responsibilities of the specific role, market conditions, geographic location, as well as the candidate's professional experience, key skills, and education/training.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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