AI QA Automation Engineer

We are looking for experienced QA Automation Engineers – AI-Forward Testing to support PLM and ERP initiatives in a highly collaborative, AI-driven engineering environment. These engineers will work closely with development, product, and project teams to design and scale automated testing frameworks while using AI tools such as Claude to accelerate test planning, test case generation, framework development, specification clarification, and day-to-day QA activities.

The role requires strong technical judgment and hands-on ownership of quality. AI will be used as an engineering accelerator, but engineers will remain responsible for critically reviewing generated outputs, identifying gaps, validating requirements, performing mandatory manual testing, and ensuring final release quality.

• Automation Framework Design & Development: Design, build, maintain, and expand scalable automated testing frameworks for PLM and ERP applications.
• AI-Assisted QA Engineering: Leverage AI tools such as Claude to accelerate test planning, test case generation, automation development, framework implementation, debugging, and specification clarification.
• Test Strategy & Coverage: Define automated testing strategies and use existing repository specifications and documentation to generate, improve, and expand test coverage.
• Specification Analysis: Use interactive AI sessions and direct collaboration with engineering and product teams to clarify requirements, identify inconsistencies or missing specifications, and resolve questions efficiently.
• Quality Gatekeeping: Critically review AI-generated test cases, code, recommendations, and technical outputs to ensure they meet engineering, product, and business requirements.
• Manual Validation: Conduct mandatory manual testing, exploratory testing, peer testing, and final validation to ensure quality and reliability before release.
• Agile Collaboration: Participate actively in PM, engineering, stand-up, planning, and other project ceremonies to help maintain accurate and current requirements, specifications, and QA documentation.
• Cross-Functional Communication: Collaborate proactively with distributed engineering, product, and project teams, including developers based in India.
• Continuous Improvement: Identify opportunities to improve QA processes, engineering productivity, automation coverage, and project timelines through effective use of AI and automation.
• Technical Ownership: Apply strong engineering judgment when deciding how to use AI, what questions to ask, how to validate generated outputs, and when manual intervention or additional technical review is required.

SOW Requirement Coverage

• AI-Forward QA Lifecycle: Supports the use of AI throughout test planning, test design, test case generation, automation development, specification clarification, and QA documentation.
• Automation Framework Development: Establishes and expands automated testing frameworks for PLM and ERP applications using Playwright, Pytest, JavaScript, Postman, and related technologies.
• Specification & Documentation Quality: Uses repository specifications, product documentation, project ceremonies, and interactive AI sessions to identify gaps and maintain accurate testing requirements.
• Manual & Automated Validation: Ensures AI-generated and automated outputs are subject to engineering review, peer testing, mandatory manual validation, and final quality approval.
• Engineering Productivity: Helps reduce project timelines by improving test coverage, automation efficiency, and development productivity while maintaining strong quality standards.

Risk Mitigation

• Prevents over-reliance on AI-generated testing artifacts by requiring engineering review, manual validation, and critical evaluation of generated test cases and automation code.
• Identifies incomplete, inconsistent, or unclear specifications early in the development lifecycle, reducing downstream defects and rework.
• Maintains release quality while accelerating delivery through a balanced approach combining AI-assisted automation, peer review, and mandatory manual testing.
• Ensures automation frameworks remain scalable, maintainable, and aligned with engineering and business requirements as PLM and ERP projects evolve.

• Experience with Oracle.
• Experience testing PLM or ERP applications.
• Previous experience using Claude, Claude Code, GitHub Copilot, or other Generative AI tools within software testing or engineering workflows.
• Experience building automation frameworks through collaborative and interactive AI-assisted development.
• Experience with CI/CD and continuous testing environments.
• Experience working with globally distributed engineering teams.

Avenue Code reinforces its commitment to privacy and to all the principles guaranteed by the most accurate global data protection laws, such as GDPR, LGPD, CCPA and CPRA. The Candidate data shared with Avenue Code will be kept confidential and will not be transmitted to disinterested third parties, nor will it be used for purposes other than the application for open positions. As a Consultancy company, Avenue Code may share your information with its clients and other Companies from the CompassUol Group to which Avenue Code’s consultants are allocated to perform its services.

• QA Automation Expertise: Strong hands-on experience in QA automation, automated test strategy, and test framework design and development.
• Automation Stack: Hands-on experience with Playwright, Pytest, JavaScript, and Postman.
• Programming Knowledge: Familiarity with Golang.
• Testing Fundamentals: Strong understanding of test planning, test case design, test execution, validation, regression testing, and release quality assurance.
• Framework Ownership: Experience designing, implementing, maintaining, and expanding automation frameworks.
• AI-Assisted Engineering: Ability to use AI tools throughout the QA lifecycle while critically reviewing and validating generated test cases, code, recommendations, and documentation.
• AI Judgment: Ability to formulate effective questions and prompts, identify unreliable or incomplete AI outputs, and determine when manual review or engineering intervention is required.
• Agile Experience: Experience working closely with Product and Engineering teams in Agile environments.
• Communication: Strong communication skills with the ability to actively participate in technical discussions, project ceremonies, stand-ups, and conversations with engineering and product stakeholders.
• Proactive Mindset: Highly motivated, collaborative, and comfortable working with evolving or incomplete specifications while proactively identifying gaps and inconsistencies.
• Distributed Teams: Comfortable working in a highly interactive environment with geographically distributed engineering teams.