AI-Enabled QA Engineer

_ 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.

Fully Remote | Must overlap US Eastern business hours through 5:00 PM ET

• 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 Dev Ops.

• Write actionable bug reports that engineers can use without requiring additional meetings.

• 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.

• 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.

• 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.

• 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.

• Develop the production-ready checklist with the Product Manager and Head of IT.

• Apply the production-ready checklist to the first tool.

• 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…