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.
• Experie