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Generative Ai Testing Jobs in Virginia (NOW HIRING)

VTG

Staff II/ Staff III AI/ML Engineer

Chantilly, VA · On-site

Experience with AI testing, validation, benchmarking, and evaluation frameworks for both traditional ML and generative AI systems * Experience implementing practical MLOps pipelines and AI ...

VTG

Staff II/ Staff III AI/ML Engineer

Chantilly, VA · On-site

Experience with AI testing, validation, benchmarking, and evaluation frameworks for both traditional ML and generative AI systems * Experience implementing practical MLOps pipelines and AI ...

Jobtailor

AI-Native Software Engineer

Arlington, VA · On-site

$140 - $190/hr

Prototype and evaluate emerging AI developer tooling (agent frameworks, code copilots, automated testing agents) * Mentor engineering teams on effective use of generative AI in development workflows

Carmax

Director, AI/ML Engineering

Richmond, VA · On-site

$181K - $290K/yr

Lead the adoption of generative AI technologies (including large language models and multimodal AI ... rapid testing, and learning, with a relentless focus on delivering customer value * Establish ...

CarMax, Inc.

Director, AI/ML Engineering

Richmond, VA · On-site

Lead the adoption of generative AI technologies (including large language models and multimodal AI ... rapid testing, and learning, with a relentless focus on delivering customer value * Establish ...

Carmax

Director, AI/ML Engineering

Richmond, VA

Lead the adoption of generative AI technologies (including large language models and multimodal AI ... rapid testing, and learning, with a relentless focus on delivering customer value * Establish ...

Jobtailor

Principal AI Engineer (Agentic AI)

Vienna, VA · On-site

$180 - $240/hr

Experience with modern Generative AI and agentic patterns (e.g., LLM workflows, orchestration ... adversarial testing. * Experience operationalizing Responsible AI and model-risk requirements ...

System One

Lead AI Software Engineer

Mclean, VA · On-site

Use Generative AI development tools such as GitHub Copilot, Cursor, Claude Code, or comparable platforms for coding, debugging, testing, refactoring, and documentation. * Ensure AI-assisted code ...

System One

Lead AI Software Engineer

Mclean, VA · On-site

Use Generative AI development tools such as GitHub Copilot, Cursor, Claude Code, or comparable platforms for coding, debugging, testing, refactoring, and documentation. * Ensure AI-assisted code ...

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All JobsGenerative Ai Testing JobsGenerative Ai Testing Jobs in Virginia

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 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 August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 18% Part Time, 2% Contract, and 1% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Staff II/ Staff III AI/ML Engineer

Vosper Thornycroft Group

Chantilly, VA • On-site

$130 - $160/hr

Other

Posted 5 days ago

Job description

Overview

VTG is seeking a highly experienced and innovative Staff AI/ML Engineer to lead the design, development, evaluation, and deployment of advanced artificial intelligence solutions in support of mission-critical and enterprise initiatives. This position is located in northern Virginia. The ideal candidate is both technically exceptional and customer-facing — capable of advising senior leadership, engaging directly with government and commercial stakeholders, and serving as a trusted authority on emerging AI technologies and best practices. This individual must have hands‑on experience building and operationalizing AI system and possess a strong understanding of modern AI governance, responsible AI principles, and evaluation methodologies.

What will you do?

Architect, design, and implement advanced AI/ML solutions, including:

  • Autonomous and semi-autonomous workflows
  • AI orchestration frameworks
  • Predictive analytics and traditional ML models

Lead the end-to-end AI lifecycle, including:

  • Data ingestion and preparation
  • Model development and fine‑tuning
  • AI testing and evaluation
  • Model deployment and monitoring
  • Operational sustainment and optimization

Develop and mature AI evaluation and testing methodologies, including:

  • Traditional ML evaluation metrics
  • Red teaming and adversarial testing
  • Bias and fairness assessments
  • Performance and reliability testing
  • Human-in-the-loop evaluation strategies

Establish and implement AI governance frameworks, including:

  • Responsible AI practices
  • Security and compliance controls
  • Model transparency and explainability
  • Risk management
  • Data governance standards
  • Collaborate across engineering, cybersecurity, cloud, data, and product teams to deliver integrated AI solutions
Do you have what it takes? Required Qualifications
  • Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Mathematics, or related technical field
  • 5+ years of experience in artificial intelligence, machine learning, software engineering, data engineering, or related technical disciplines
  • Statistical modeling and AI evaluation methodologies
  • Experience with AI testing, validation, benchmarking, and evaluation frameworks for both traditional ML and generative AI systems
  • Experience implementing practical MLOps pipelines and AI operationalization frameworks
  • Strong programming experience with: Python, Jupyter Notebooks or equivalent notebook environments
  • Experience with big data and distributed processing technologies such as: Apache Spark, Databricks (preferred)
  • Experience with one or more major cloud platforms: Microsoft Azure, Amazon Web Services (AWS), Google Cloud Platform (GCP)
  • Familiarity with: Containerization and orchestration technologies CI/CD pipelines for AI deployments
  • Strong communication and presentation skills with demonstrated customer‑facing experience
  • Ability to translate complex technical concepts into actionable business and mission solutions
Preferred Qualifications
  • Master’s degree or PhD
  • Experience supporting Federal Government, DoD, Intelligence Community, or highly regulated environments
  • Experience implementing secure AI architectures in classified or sensitive environments
  • Expertise in modern AI/ML architectures, including agentic AI systems, large language models (LLMs), autonomous workflows, AI evaluation frameworks, and production‑grade machine learning operations (MLOps)
  • Design scalable MLOps and AIOps pipelines to support secure and repeatable deployment of AI capabilities in enterprise and cloud environments
  • Demonstrated experience architecting and deploying enterprise‑scale AI/ML solutions in production environments
  • Hands‑on experience building and operationalizing:Agentic AI systems LLM-powered applications; AI orchestration frameworks; Autonomous decision‑support systems
  • Familiarity with AI security, adversarial AI, and zero trust principles
  • Experience with GPU infrastructure, model optimization, and scalable inference architectures
  • Familiarity with: Vector databases; AI orchestration frameworks (LangChain, Semantic Kernel, CrewAI, AutoGen, etc.)
  • Serve as a senior technical advisor to customers, executives, and program leadership on AI strategy, architecture, modernization, and emerging capabilities
  • Lead technical discussions, architecture reviews, demonstrations, and customer briefings with confidence and authority
  • Stay current with emerging AI research, industry trends, open‑source technologies, and commercial AI platforms; continuously assess applicability to organizational and customer needs
  • Published research, conference presentations, patents, or contributions to the AI community preferred
  • Active participation in AI research communities, industry working groups, or open‑source AI initiatives
  • Mentor engineers, data scientists, and software developers on AI best practices, architectures, and implementation strategies
Clearance Requirement
  • Active Secret security clearance required, or ability to obtain and maintain a Secret clearance.
Desired Characteristics
  • Strategic thinker with strong technical depth and hands‑on engineering capability
  • Passion for continuous learning and staying ahead of rapidly evolving AI technologies
  • Comfortable operating in ambiguous and fast‑paced technical environments
  • Strong leadership, collaboration, and mentoring abilities
  • Customer‑focused with executive presence and consultative communication skills
Technologies & Tools
  • Python
  • Jupyter Notebook
  • Apache Spark
  • Databricks
  • TensorFlow
  • PyTorch
  • Hugging Face
  • LangChain
  • Semantic Kernel
  • CrewAI
  • AutoGen
  • Kubernetes
  • Docker
  • Azure AI Services
  • AWS SageMaker
  • Google Vertex AI
  • Vector databases
  • MLflow
  • GitLab/GitHub CI/CD pipelines
Work Environment

This role may support hybrid, on‑site, or customer‑location work environments depending on program requirements. Occasional travel may be required for customer engagement, technical workshops, or industry events.

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