AI Quality Architect: GenAI-Powered QA & Architecture

EPAM Systems Inc; United States; À distance; USD 140 000 - 210 000; Plein temps

Résumé du poste

EPAM Systems Inc is seeking an AI Quality Architect to own customer objectives and technical requirements, design full-cycle QA infrastructure, and determine the components for the product. You will build proofs of concept, engage in pre-sales activities, consult with clients, and audit existing solutions for enhancements.

Responsibilities include designing large-scale QA architectures, collaborating with stakeholders, and driving AI-powered QA through GenAI, LLMs, and AI agents to boost test

Qualifications

• 10+ years of IT experience in QA or testing environments.
• Proven Automation Architect experience leading architectures.
• Experience with GenAI tools and AI/ML testing.
• Proficiency in Java, C#, or Python.
• Familiarity with JavaScript/TypeScript.

Responsabilités

• Design large-scale QA solutions with in-depth technical detail.
• Collaborate with business stakeholders to determine requirements.
• Define and execute comprehensive test automation strategies and roadmaps.
• Build and maintain QA frameworks and shared formats for programs.
• Guide architecture frameworks for test automation across projects.
• Track key quality metrics like defect density and test coverage.
• Lead continuous improvement of test processes and tools for digital transformation.
• Participate in pre-sales activities such as proposals and demos.

Connaissances

Automation Architect

GenAI tools

Java/C#/Python

JavaScript/TypeScript

Leadership in Architecture

Outils

DeepEval

Ragas

Description du poste

EPAM Systems Inc is seeking an AI Quality Architect to own customer objectives and technical requirements, design full-cycle QA infrastructure, and determine the components for the product. You will build proofs of concept, engage in pre-sales activities, consult with clients, and audit existing solutions for enhancements.

Responsibilities include designing large-scale QA architectures, collaborating with stakeholders, and driving AI-powered QA through GenAI, LLMs, and AI agents to boost test