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Generative Ai Testing Jobs in Baltimore, MD (NOW HIRING)

Cynet Systems

QA Test Engineer with AI/ML

Columbia, MD · On-site

$57.40 - $62.40/hr

Conduct deep technical analysis and testing of AI/ML and Generative AI systems, including validation and verification of model outputs, prompt behavior, and end-to-end application workflows.

CYNET SYSTEMS

QA Test Engineer with AI/ML

Columbia, MD · On-site

$57 - $62/hr

Conduct deep technical analysis and testing of AI/ML and Generative AI systems, including validation and verification of model outputs, prompt behavior, and end-to-end application workflows.

CYNET SYSTEMS

QA Test Engineer with AI/ML

Columbia, MD · On-site

$57 - $62/hr

Conduct deep technical analysis and testing of AI/ML and Generative AI systems, including validation and verification of model outputs, prompt behavior, and end-to-end application workflows.

Morgan Stanley

VP, AI Compliance Officer

Baltimore, MD · On-site

$108K - $185K/yr

What you'll do in the role: > Advise Wealth Management Generative AI and Platforms teams on AI ... Testing teams. > Collaborate with Policy and Training teams to draft, implement, and maintain ...

Morgan Stanley

VP, AI Compliance Officer

Baltimore, MD · On-site

$108K - $185K/yr

What you'll do in the role: > Advise Wealth Management Generative AI and Platforms teams on AI ... Testing teams. > Collaborate with Policy and Training teams to draft, implement, and maintain ...

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Generative Ai Testing information

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How much do generative ai testing jobs pay per hour?

As of Sep 9, 2026, the average hourly pay for generative ai testing in Baltimore, MD is $53.38, according to ZipRecruiter salary data. Most workers in this role earn between $43.94 and $61.15 per hour, depending on experience, location, and employer.

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 Baltimore, MD?

For Generative Ai Testing jobs in Baltimore, MD, the most frequently searched job titles are:

What job categories do people searching Generative Ai Testing jobs in Baltimore, MD look for?

The top searched job categories for Generative Ai Testing jobs in Baltimore, MD are:

Generative Ai Testing jobs near you

AI/ML & Gen AI Testing -- W2 ONLY

Columbia, MD • On-site

nTech Solutions
See more jobs at nTech Solutionsexternal

Other

Posted 5 days ago

Job description

BEFORE APPLYING FOR THE JOB. KINDLY READ THE COMPLETE JOB DESCRIPTION PROPERLY(EVEN THE TERMS OF EMPLOYMENT ALSO), NO 1099, C2C , H1 TRANSFER

Title: AI/ML & Gen AI Testing

Location: Hybrid (1 day/week on-site in Merriweather, MD; occasional travel to Northern VA / DC offices)

Terms of Employment:

W2 Contract, 6-Months (possibile of extension)

Location: Hybrid (1 day/week on-site in Merriweather, MD; occasional travel to Northern VA / DC offices)

Candidate must reside in DMV area only

Role Overview

This role sits at the intersection of software/QA testing and AI/ML it is not a hands-on model-building or deployment position. The engineer will work closely with the lead systems engineer and lead developer supporting two active workstreams:

1. Machine Learning Models supporting existing and new ML initiatives, evaluating fine-tuned and agentic model outputs, and providing iterative feedback to improve model efficiency and accuracy in collaboration with the business team.

2. Generative AI Chatbots (Bridge Console) testing chatbot responses for accuracy and hallucination, crafting and refining prompts, and working with the development team to fine-tune outputs based on findings.

Day-to-day work includes testing across multiple pipeline stages (evaluating intermediate outputs at each processing step, not just final results), analyzing model/chatbot outputs, and partnering with both engineering and business stakeholders to validate results.

Required Qualifications (Mandatory)

3+ years of software engineering / QA testing experience

1 2 years of hands-on experience with Generative AI concepts

3+ years of cloud platform experience (AWS, Azure, or Google Cloud Platform)

Working knowledge of system/software testing methodology

~1 year of experience with BDD, Selenium, and test automation

Understanding of relational and non-relational databases, with experience working with data pipelines (e.g., Oracle, Snowflake)

Hands-on exposure to prompt engineering

Strong analytical skills comfortable interpreting model outputs and providing structured feedback

Experience with Jira and Confluence

Preferred Qualifications

Familiarity with AWS Bedrock and/or SageMaker

Background in healthcare or insurance industry

Exposure to newer AI/ML testing frameworks

Salesforce experience (nice to have, not required)

Interview Process

1. Initial video screen (~30 minutes)

2. In-person interview (typically at Merriweather, location may vary)

Apply