This role is open to candidates based in LATAM, Africa, and Eastern Europe. Please note that as this role supports U.S.-based clients, candidates must be available to work during U.S. business hours aligned with the client’s time zone. The AI-Enabled QA Engineer will test the tools supporting acquisitions, financial analysis, approval workflows, and marketing operations, ensuring they are accurate and production-ready. This role will write automated checks, test edge cases, validate AI-generated outputs against source data, and help define what “production-ready” means for the organization. The AI-Enabled QA Engineer will work closely with the engineer, product manager, M&A and marketing leads, and the Head of IT. Location Fully Remote | Must overlap US Eastern business hours through 5:00 PM ET Key Responsibilities QA & Test Automation • Build test automation using Playwright, Cypress, or Selenium for interfaces and pytest or equivalent for underlying systems. • Design test cases from specifications using equivalence classes, boundaries, negative cases, and cases not covered by the original specification. • Run tests through CI pipelines so checks execute automatically. • Track and triage defects using Jira, Linear, or Azure DevOps. • Write actionable bug reports that engineers can use without requiring additional meetings. Data Validation & API Testing • Use SQL to independently verify data through joins, aggregation, and record-level investigation. • Validate data at scale through row counts, duplicate detection, orphaned records, referential integrity, and reconciliation of totals. • Test APIs using Postman or code. • Validate status codes, payload structures, authentication failures, and behavior on repeated identical requests. AI Output & Document Testing • Evaluate AI-generated output using a fixed set of examples. • Measure precision and recall using historical cases. • Run regression tests following prompt changes. • Check AI-generated output for fabricated facts. • Verify that fields extracted from financial statements can be traced back to their source documents. • Test document extraction against scenarios such as scanned documents, rotated pages, and missing signatures. • Verify target-analysis summaries against source financials so every number in an AI-generated summary is traceable to a document. Workflow & Notification Testing • Test the diligence data room against cases including missing documents, duplicate uploads, and rooms that appear complete but are not. • Test notifications to ensure they trigger when a room stalls without generating unnecessary alerts. • Test the approval step on the priority tracker to ensure nothing is submitted without review, including attempts to bypass the approval process. • Write test cases for AI-generated outputs that every change must continue to pass. Brand Asset Quality Assurance • Check generated brand assets across eight to ten new brands. • Verify that each generated asset contains the correct brand name, logo, and colours. • Ensure assets do not contain branding belonging to another brand. Production Readiness • Develop the production-ready checklist with the Product Manager and Head of IT. • Apply the production-ready checklist to the first tool. Qualifications Experience • 3+ years of QA experience across both manual and automated testing. • Experience testing data-heavy applications rather than only user interfaces. • Experience with test automation in code. • Experience evaluating AI-generated outputs methodically. • Experience with data validation and API testing. • Experience testing document-heavy or extraction-based systems is a plus. • Experience checking creative or brand output for consistency is a plus. • Healthcare data experience is a plus. • Finance or accounting testing experience is a plus. • Test automation experience within a Microsoft environment is a plus. • Experience creating a definition of done that an organization adopted is a plus. Skills • Hands-on ability to use Playwright, Cypress, or Selenium for interface testing. • Ability to use pytest or equivalent for automated testing. • SQL skills for joins, aggregation, and independent data verification. • Ability to test APIs using Postman or code. • Strong test-case design skills covering equivalence classes, boundaries, negative cases, and overlooked scenarios. • Ability to validate row counts, duplicates, orphaned records, referential integrity, and data reconciliation. • Ability to evaluate AI output using golden sets, precision and recall, regression testing, and fabricated-fact checks. • Ability to test document extraction and trace extracted information back to source documents. • Ability to run automated tests through CI pipelines. • Ability to track and triage defects using Jira, Linear, or Azure DevOps. • Ability to write clear, actionable bug reports. • Understanding that AI-generated outputs can fail confidently, plausibly, and inconsistently. • Ability to document processes and testing standards. • Performance testing, synthetic test-data generation, accessibility checks, and Power Platform testing are additional skills that are beneficial but not required. What Success Looks Like Within the first 90 days: • The diligence data room is tested against realistic edge cases. • Target-analysis summaries are verified against source financials. • The notification system is tested for both required notifications and unnecessary alerts. • The approval step on the priority tracker prevents unreviewed submissions. • A fixed set of test cases is established for AI-generated output. • Generated assets across eight to ten brands are checked for correct brand names, logos, and colours. • A production-ready checklist is written with the Product Manager and Head of IT and applied to the first tool. Opportunity The AI-Enabled QA Engineer will work directly with the engineer, Product Manager, M&A and marketing leads, and Head of IT to establish testing standards