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Generative Ai Testing Jobs in Virginia (NOW HIRING)

Photon

GenAI Solution Architect - VA

Norfolk, VA

$61 - $80.25/hr

We are seeking an AI Engineer to work on a Generative AI initiative to join our team. The ideal ... Lead all data experiments under the iterative testing framework. Engage with stakeholders to ...

InterImage

AI/ML Software Developer

Arlington, VA · On-site

Integrate Generative AI capabilities into existing platforms and user experiences * Evaluate and ... Strong experience developing Python applications from scratch, including testing and deployment

InterImage

AI/ML Software Developer

Arlington, VA · On-site

Integrate Generative AI capabilities into existing platforms and user experiences * Evaluate and ... Strong experience developing Python applications from scratch, including testing and deployment

Photon

GenAI Solution Architect - VA

Norfolk, VA · On-site

$61 - $80.25/hr

We are seeking an AI Engineer to work on a Generative AI initiative to join our team. The ideal ... Lead all data experiments under the iterative testing framework. Engage with stakeholders to ...

Think Consulting

Sr Data & AI Engineer

Virginia Beach, VA · On-site +1

$101K - $121K/yr

This role sits at the intersection of data engineering, machine learning, and generative AI, with a ... Apply best practices in CI/CD, version control (Git), automated testing, and agile software ...

Peraton

QA/Test Engineer

Herndon, VA

$86K - $138K/yr

This role focuses on the unique challenges of testing generative AI-driven systems, including non-deterministic outputs, analytical accuracy validation, and regression testing for AI behavior changes.

Peraton

QA/Test Engineer

Herndon, VA · On-site

$86K - $138K/yr

This role focuses on the unique challenges of testing generative AI-driven systems, including non-deterministic outputs, analytical accuracy validation, and regression testing for AI behavior changes.

Steampunk

AI Evaluation Scientist

Mclean, VA

$105K - $145K/yr

... generative AI models (LLMs preferred). * Familiarity with evaluation metrics, statistical testing, dataset creation, and experimental design for AI systems. * Proficiency in Python and relevant ...

Steampunk

AI Evaluation Scientist

Mclean, VA · On-site

$105K - $145K/yr

... generative AI models (LLMs preferred). * Familiarity with evaluation metrics, statistical testing, dataset creation, and experimental design for AI systems. * Proficiency in Python and relevant ...

Infosys

AI ML Engineer with LLM & AWS Integration

Reston, VA · On-site

$108K - $145K/yr

... LLMs and generative AI, while also integrating and deploying models using AWS services ... code, testing techniques, and support activities to enrich the knowledge base and assist other ...

Steampunk

AI Evaluation Scientist

Mclean, VA

$105K - $145K/yr

... generative AI models (LLMs preferred). * Familiarity with evaluation metrics, statistical testing, dataset creation, and experimental design for AI systems. * Proficiency in Python and relevant ...

Orbis Operations

AI Research Intern

Mclean, VA · On-site

... generative AI. * Assist in designing, implementing, and evaluating machine learning models and ... Prepare datasets for training, testing, and validation. * Perform statistical analysis and model ...

InterImage

Software Engineer

Arlington, VA · On-site

Support the design, development, testing, and deployment of modern software applications and AI-enabled tools * Assist with integrating Generative AI technologies into internal and customer-facing ...

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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 Virginia? For Generative Ai Testing jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Generative Ai Testing jobs in Virginia look for? The top searched job categories for Generative Ai Testing jobs in Virginia are:
What cities in Virginia are hiring for Generative Ai Testing jobs? Cities in Virginia with the most Generative Ai Testing job openings:
Generative Ai Testing jobs near you
Infographic showing various Generative Ai Testing job openings in Virginia as of June 2026, with employment types broken down into 10% Internship, 70% Full Time, 15% Part Time, and 5% Temporary. Highlights an 80% In-person, 5% Hybrid, and 15% Remote job distribution.
GenAI Solution Architect - VA

GenAI Solution Architect - VA

Photon

Norfolk, VA

$61 - $80.25/hr

Other

Posted 19 days ago

Job description

We are seeking an AI Engineer to work on a Generative AI initiative to join our team. The ideal candidate will have a deep understanding of language models, text-to-image, and other generative AI models. They must possess knowledge of Python, and machine learning frameworks. 

Responsibilities 

Develop and implement generative AI models, including LLMs, text-to-image and generative AI models. 

Train and evaluate models using large datasets. 

Troubleshoot and debug code to ensure high-quality results. 

Keep up to date with the latest developments in the field of generative AI and apply relevant new knowledge to the company's AI projects. 

Deep understanding of how to scale models and their limitations. Ability to quickly identify opportunities for model improvement 

Use predictive modeling to enhance and optimize customer experiences, revenue generation, ad targeting, and other business outcomes. 

Collaborate with different teams to implement models and monitor outcomes. 

Develop processes and tools to monitor and analyze model performance and data accuracy. 

Lead all data experiments under the iterative testing framework. 

Engage with stakeholders to understand business challenges and develop data-driven solutions. 

Conduct advanced data analysis and design highly complex algorithmic models. 

Communicate complex quantitative analysis in a clear, precise, and actionable manner. 
 

Requirements 

Bachelor's or Master's degree in Computer Science, Engineering, or a related field. 

2+ years of experience in developing and training AI/ML models. 

Experience with data querying languages like SQL, scripting languages like Python, and/or statistical/mathematical software e.g. R 

Knowledge of state-of-the-art generative AI models such as GPT-3, DALL-E, and CLIP. 

Experience with Cloud infrastructure and Platforms - Azure /GCP/AWS 

Experience with training and evaluating large-scale models on high-performance computing clusters. 

Strong understanding of deep learning, natural language processing, and computer vision. 

Excellent problem-solving skills and ability to work independently and in a team environment. 

Preferred Qualifications: 

Bachelor's or master's degree in computer science, Data Science, Statistics, Math, Physics, or other Science related discipline with course work in AI/ML. 

Demonstrated experience in developing and training AI models  

Strong knowledge of deep learning, natural language processing, and computer vision. 

Experience with scaling up generative models and deploying them in production environments. 

Ability to work collaboratively in a team environment and communicate complex technical concepts to non-technical stakeholders. 

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