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Generative Ai Testing Jobs in New Jersey (NOW HIRING)

Johnson & Johnson

Data and Generative AI Engineer

Raritan, NJ · On-site

$117K - $140K/yr

Design, develop and deliver Generative/Agentic AI solutions that accelerate End-to-End Data ... Ensure data quality and integrity through meticulous testing and validation, applying Intelligent ...

Jj

Data and Generative AI Engineer

Raritan, NJ · On-site

$117K - $140K/yr

Design, develop and deliver Generative/Agentic AI solutions that accelerate End-to-End Data ... Ensure data quality and integrity through meticulous testing and validation, applying Intelligent ...

Jj

Data and Generative AI Engineer

New Brunswick, NJ · On-site

$118K - $141K/yr

Design, develop and deliver Generative/Agentic AI solutions that accelerate End-to-End Data ... Ensure data quality and integrity through meticulous testing and validation, applying Intelligent ...

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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 New Jersey?

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What job categories do people searching Generative Ai Testing jobs in New Jersey look for?

The top searched job categories for Generative Ai Testing jobs in New Jersey are:

What cities in New Jersey are hiring for Generative Ai Testing jobs?

Cities in New Jersey with the most Generative Ai Testing job openings:

Generative Ai Testing jobs near you
Infographic showing various Generative Ai Testing job openings in New Jersey as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 10% Part Time, 2% Contract, and 1% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

AI Solutions Engineer (Snowflake & Generative AI)

S Linx LLC

Princeton, NJ • On-site

Other

Posted 28 days ago

Job description

Job Title: AI Solutions Engineer (Snowflake & Generative AI)

Location: Princeton, NJ Locals only

Primary Responsibilities

·        Design and develop AI-powered applications using Snowflake Cortex, Large Language Models (LLMs), and Agentic AI frameworks.

·        Build and optimize semantic models and AI-ready datasets for structured and unstructured data.

·        Design and automate data ingestion, transformation, orchestration, and refresh pipelines.

·        Integrate clinical, commercial, and external data sources into enterprise AI workflows.

·        Optimize Snowflake performance and ensure production-ready, maintainable solutions.

·        Support deployment, testing, and operationalization of AI solutions.

Required Qualifications

·        7+ years of experience in Data Engineering, AI Engineering, or Software Engineering.

·        Strong hands-on experience with Snowflake, including Snowpark, SQL, and performance optimization.

·        Experience developing Generative AI applications using LLMs, Prompt Engineering, RAG, AI Agents, and workflow orchestration.

·        Strong programming skills in Python.

·        Experience designing semantic data models and enterprise data pipelines.

Preferred Qualifications

·        Experience with Snowflake Cortex (Analyst, Search, Complete, Intelligence, Cortex Agents).

·        Experience with healthcare or life sciences data.

·        Experience with OMOP, claims data, clinical trial data, or commercial pharmaceutical data.

Technical Skills

·        Snowflake (Snowpark, Cortex, SQL)

·        Python

·        Generative AI / LLMs

·        RAG & AI Agents

·        Prompt Engineering

·        Semantic Modeling

Nice-to-Have Experience

·        Commercial Pharma Analytics

·        Clinical Trials

·        Claims Data

·        OMOP Data Model

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