> Markdown version of [/jobs/ext/2942736-qa-automation-engineer](https://www.wearedevelopers.com/jobs/ext/2942736-qa-automation-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # QA Automation Engineer - **Company:** WAVERLEY SOFTWARE, INC. - **Location:** United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Amazon Web Services, Open Source Technology, Salesforce.Com, Systems Integration, AI Platforms - **Published:** September 16, 2026 - **Apply:** https://waverleysoftware.com/careers/senior-qa-automation-engineer#apply ## About the Role * An engineering background moving into QA automation. * 5+ years of test automation or software engineering experience. * Hands-on experience with Playwright * Comfort working across multiple stacks/surfaces, including web and ideally Salesforce. * Strong spoken and written English. * A proactive, self-directed style: comfortable flagging blockers, communicating async, and taking initiative in a fast-moving greenfield project. * Experience with system integrations and orchestration. * Strong communication skills, with the ability to articulate complex data science concepts and architectural trade-offs to client-side technical leadership (CTOs, Lead Data Scientists)., * Experience with open-source model deployment (Llama 3, Mistral) and fine-tuning techniques. * Familiarity with managed cloud AI services (AWS SageMaker, Vertex AI, or Azure AI). ## Description * Build out the automation framework alongside the Lead Architect. * Apply the framework retroactively across the existing codebase to lift automated coverage toward the 60-70% target. * Support other QA engineers as they adopt the new framework. * Deliver automated tests within the same sprint as the feature code they cover. * Production Reliability & Monitoring: Ensure ongoing model scalability, reliability, and robust monitoring in production environments. * Performance & Cost Optimization: Proactively identify and implement strategies to optimize both the computational performance and operational costs of AI systems.