$ cat jobs/ai-engineer-quality-celonis-0eb03342d47b.json
AI Engineer - Quality
Celonis is the global leader in Process Intelligence and the pioneer of Process Mining technology. As one of the world’s fastest-growing enterprise SaaS companies, we are changemakers pushing the boundaries of what’s possible. We invest heavily in advanced AI capabilities—specifically our Process Intelligence Graph—to turn data insights into immediate business action. We believe there is a massive opportunity to unlock global productivity and sustainability by placing intelligence at the core of every business process. Join our mission to make processes work for people, companies, and the planet. The Team: You will join the Sailfin Accounts Receivable (AR) Team , a product engineering team within the Finance & Accounting domain. We build and maintain an enterprise-grade AR product used by global customers. The team works with modern engineering practices, AI/ML capabilities, automation, and deep integrations across platforms such as Salesforce, Celonis, and other enterprise systems. This role sits at the intersection of Quality Engineering + AI-driven automation + enterprise data integration . The Role: We are looking for an AI Engineer – Quality who can build next-generation intelligent automation and quality frameworks. This role goes beyond traditional QA—it combines strong automation engineering, enterprise integration understanding, and AI-assisted quality improvement. You will architect automation frameworks, drive end-to-end quality strategy, and build smart testing capabilities powered by LLMs and AI agents. Most importantly, you will test the bridge between the world’s #1 CRM (Salesforce) and the world’s #1 Process Mining platform (Celonis) —a complex, data-heavy integration that powers enterprise decision-making. The work you’ll do: Architect the Automation Foundation Design, build, and maintain a scalable, modular automation framework from scratch using Playwright (JavaScript/TypeScript) . Establish reusable utilities, reporting systems, abstraction layer
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