This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Lead AI Quality Engineer based in United States.
The Lead AI Quality Engineer will define and advance the quality strategy for software and AI-powered products in a highly technical, AI-accelerated delivery environment.
This role combines hands-on quality engineering with practice leadership, setting standards that help teams maintain high-quality software as development velocity increases.
You’ll establish testing methodologies, automation practices, evaluation frameworks, and responsible AI oversight across complex digital and AI engagements.
The position is split between client delivery and quality practice leadership, giving you direct influence on both customer outcomes and internal engineering standards.
You’ll evaluate AI systems for accuracy, relevance, consistency, hallucinations, and bias while also applying AI-enabled techniques to improve testing of traditional software.
The role involves close collaboration with Engineering, Product, Project Management, Strategy, Sales, Marketing, clients, and other quality professionals.
You’ll also mentor quality specialists and engineers, helping build a modern quality culture that treats testing as an integral part of the entire software development lifecycle.
Accountabilities:
- Define the vision and roadmap for software quality in an AI-accelerated development environment, establishing standards for requirements, development, testing, and delivery.
- Evaluate existing QA practices, methodologies, automation frameworks, AI-enabled testing tools, and traditional testing capabilities, maintaining a roadmap for continuous improvement.
- Establish testing standards and defaults that determine when testing should be performed manually, through automation, or through AI-powered approaches, including appropriate human oversight.
- Identify and implement tools and methodologies that enable quality assurance to keep pace with increasingly rapid software development.
- Lead the responsible use of agentic AI for automated test creation, teaching engineers and other team members how to generate and maintain effective automated tests.
- Define how quality should be integrated throughout the software development lifecycle, including when and how quality specialists should participate in project activities.
- Develop and maintain standards for testing AI-powered features, including LLM-based applications, agentic workflows, and machine learning components.
- Build evaluation frameworks for AI outputs that assess accuracy, relevance, consistency, hallucination detection, and potential bias across protected attributes.
- Build, operate, maintain, and enhance agentic QA tools and contribute them to broader AI delivery frameworks.
- Test internally developed AI tools and provide specific, actionable findings that help improve their reliability, usability, and quality.
- Own the integration of testing into automated development workflows, including unit, functional, integration, visual regression, and AI-enabled testing within CI/CD pipelines.
- Lead quality strategy and execution across client projects, presenting quality outcomes directly to client stakeholders and advising teams on quality best practices.
- Coach client engineering teams toward a quality ownership mindset and help connect technical quality outcomes to business objectives.
- Manage, mentor, and coach Quality Specialists and Quality Engineers while supporting their technical development and adoption of emerging testing methodologies.
- Educate cross-functional teams on evolving QA standards and approaches so that project management, strategy, sales, and marketing can clearly understand and communicate the quality practice.
- Stay current with AI testing methodologies, responsible AI principles, and emerging quality engineering tools, proactively sharing relevant knowledge with the wider team.
- Measure success through faster and more efficient testing, documented QA standards, stronger internal tools, improved team capabilities, reliable AI systems, and business-focused quality reporting.
Requirements:
- Bring 8+ years of software QA or quality engineering experience, preferably including hands-on testing of AI- or ML-powered features.
- Demonstrate experience managing, mentoring, and coaching direct-report employees or quality professionals.
- Have a proven ability to assess QA practices and tools, recommend improvements, and lead the implementation of those improvements.
- Possess strong automation skills and experience writing automated tests using at least one testing framework.
- Demonstrate working knowledge of AI testing concepts, including hallucination detection, AI output evaluation, prompt engineering for test generation, and bias testing.
- Understand how to design quality coverage around business-critical outcomes rather than simply executing predefined test plans.
- Be comfortable working hands-on with automation frameworks, evaluation pipelines, AI-enabled testing tools, and unfamiliar technology stacks.
- Have strong verbal and written communication skills, with the ability to present quality findings and business outcomes to both technical and non-technical stakeholders.
- Bring experience with WCAG 2.1 or WCAG 2.2 accessibility standards.
- Demonstrate the ability to work effectively in client-facing consulting environments and adapt quickly to different delivery models, technologies, and project requirements.
- Agency, digital consultancy, technology consulting, or professional services experience is preferred.
- Possess strong curiosity, adaptability, continuous-learning ability, and an interest in emerging AI technologies and quality engineering practices.
Benefits:
- Annual salary of $120,000–$145,000 USD.
- Fully remote work within the United States.
- Hybrid allocation of client delivery and quality practice leadership, providing both hands-on technical work and strategic ownership.
- Opportunity to shape quality engineering standards and AI testing practices across multiple digital engagements.
- Direct client exposure and the opportunity to advise engineering teams and senior stakeholders.
- Leadership responsibility for mentoring and developing quality professionals.
- Ongoing exposure to emerging AI, agentic development, AI evaluation, and modern software testing technologies.
- Opportunity to contribute to internally developed AI and QA tools and frameworks.
- Collaborative, remote-first environment focused on professional growth, knowledge sharing, and high-quality delivery.
- Opportunity to influence how quality engineering evolves alongside AI-accelerated software development.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
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