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Generative Ai Testing Jobs in North Carolina (NOW HIRING)

Apex Systems

Engineer 5 - Penn Tester

Charlotte, NC · On-site

$57.50 - $76.75/hr

Develop adversarial testing methodologies for: * Large Language Models (LLMs) * Generative AI Applications * AI-Powered Services * Design defenses against: * Prompt Injection Attacks * Model Abuse

Tata Consultancy Service Limited

Analyst

Charlotte, NC · On-site

$90K - $107K/yr

Design end-to-end test plans for generative AI, LLMs, and machine learning models. • Model ... Build advanced test automation frameworks using Python, PyTest, and specialized AI testing tools ...

Tata Consultancy Service Limited

QA Engineer

Charlotte, NC · On-site

$100K - $120K/yr

Design end-to-end test plans for generative AI, LLMs, and machine learning models. • Model ... Build advanced test automation frameworks using Python, PyTest, and specialized AI testing tools ...

Tata Consultancy Service Limited

Analyst

Charlotte, NC · On-site

$90K - $107K/yr

Design end-to-end test plans for generative AI, LLMs, and machine learning models. • Model ... Build advanced test automation frameworks using Python, PyTest, and specialized AI testing tools ...

Judge Group, Inc.

Engineer

Charlotte, NC · On-site

$89 - $95/hr

Experience securing Generative AI (GenAI) and Large Language Model (LLM) applications. * Expertise in adversarial AI testing, prompt injection mitigation, and AI security frameworks. * Experience ...

Noblesoft Technologies

GEN AI Architect

Charlotte, NC · On-site

Lead the architectural design and implementation of scalable, generative AI-powered solutions for ... Establish and automate DevOps pipelines for model deployment, testing and iterative enhancements.

CYNET SYSTEMS

Gen AI Engineer

Charlotte, NC · On-site

$57/hr

Familiarity with generative AI frameworks and LLMs. * Knowledge of natural language processing (NLP ... Familiarity with modularized, testable code and unit testing frameworks. * Appetite for continuous ...

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Generative Ai Testing information

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 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 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 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. Relevant certifications and hands-on projects can enhance your qualifications for roles in AI testing environments.

Is Generative AI Testing a good career?

Generative AI Testing is a growing field within AI development, focusing on evaluating the quality and safety of AI-generated content. It requires skills in machine learning, programming, and understanding AI models, often involving tools like Python and TensorFlow. The role offers opportunities in tech companies and research labs, with demand expected to increase as AI applications expand.

What are popular job titles related to Generative Ai Testing jobs in North Carolina?

For Generative Ai Testing jobs in North Carolina, the most frequently searched job titles are:

What job categories do people searching Generative Ai Testing jobs in North Carolina look for?

The top searched job categories for Generative Ai Testing jobs in North Carolina are:

Generative Ai Testing jobs near you
Infographic showing various Generative Ai Testing job openings in North Carolina as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, 3% Contract, and 1% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Senior AI Engineer (Generative AI & Intelligent Automation)

Arkhya Tech

Charlotte, NC • On-site

$101K - $133K/yr

Other

Posted 12 days ago

Job description

Role: Senior AI Engineer (Generative AI & Intelligent Automation)

Work Location : Charlotte, NC (Hybrid) F2F interview

Position : C2C

Job description:

Required Qualifications

  • 5+ years of experience in Artificial Intelligence (AI), Machine Learning (ML), Generative AI, or Intelligent Automation solutions.
  • 5+ years of hands-on experience with Python and AI development frameworks, including LangChain, LangGraph, Google ADK, and agent-based architectures.
  • 3+ years of experience implementing Document AI / Intelligent Document Processing (IDP) solutions involving OCR, PDF parsing, data extraction, confidence scoring, rule-based validation, semantic matching, fuzzy comparison, and API integration.
  • 3+ years of experience designing and developing REST APIs, Microservices, and JSON/XML schema mappings for enterprise integrations.
  • Strong experience building and deploying LLM-powered applications, AI agents, RAG (Retrieval-Augmented Generation) solutions, and workflow automation platforms.
  • Experience implementing confidence scoring, validation frameworks, human-in-the-loop (HITL) workflows, and exception handling mechanisms.
  • Familiarity with workflow orchestration platforms, business process automation, and event-driven architectures.
  • Strong understanding of data privacy, PII handling, AI governance, model explainability, auditability, and responsible AI controls.
  • Experience with prompt engineering, vector databases, embeddings, and semantic search technologies.
  • Proficiency in Git, CI/CD pipelines, Agile methodologies, and automated testing practices.
  • Familiarity with MongoDB, Neo4j Knowledge Graphs, Redis, and vector databases.
  • Knowledge of MLOps, AI observability, model monitoring, and evaluation frameworks.
  • Strong analytical, troubleshooting, and problem-solving skills.
  • Excellent communication and collaboration abilities within cross-functional teams.

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