Maintain and evolve advanced automation test frameworks, ensuring reliability and scalability across AI-powered applications.
Design intelligent test strategies for cutting-edge AI/ML features, partnering closely with developers to proactively identify risk and automate validation.
Craft and execute comprehensive test plans that validate both traditional workflows and AI-driven experiences—then automate them for speed and scale.
Analyze and report on quality metrics, turning data into actionable insights that help shape the future of our AI platforms.
Support engineering teams in debugging complex issues across applications and dev/test environments, especially those involving model behavior or inference edge cases.
Requirements & Skills:
Experience in leveraging or critically thinking about how to integrate AI into work processes, decision-making, or problem-solving. This may include using AI-powered tools, automating workflows, analyzing AI-driven insights, or exploring AI’s potential impact on the function or industry.
12+ years of professional experience in quality engineering or application development.
Educational equivalency: 12 years of related experience with a Bachelor’s degree, 8 years with a Master’s, or 5 years with a PhD; or equivalent practical experience.
Advanced knowledge and hands-on expertise in quality engineering practices.
Proven experience leading QE efforts within agile environments, including scrum teams of 10+ engineers.
Skilled in issue diagnosis and resolution, including relief strategy, root cause analysis (RCA), and permanent fixes.
Proficient in modern development environments, including cloud platforms, web technologies, frameworks, and service-oriented architecture.
Strong ability to apply best practices and enforce coding standards aligned with role-specific requirements, including programming, network, functional design, and algorithmic implementation.
Fluent in scripting with JavaScript; experienced with Eclipse, Jenkins, Maven, and Git.
Deep understanding of testing methodologies—including performance, unit, integration, and automated testing—with the ability to apply each effectively based on context.
Expert in development tools and environments: IDEs, debuggers, build tools, version control, Unix administration tools, profilers, and ServiceNow instances.
Adept at driving cross-functional technical discussions with a solid grasp of software development lifecycle (SDLC) principles.
