Senior Quality Assurance Lead - AI and Platform Validation

Senior Quality Assurance Lead - AI and Platform Validation

Duration: 12+ Months

Location: Remote

We are seeking a Senior Quality Assurance Lead to own computer system validation for an AI-driven enterprise application and its supporting Google Cloud environments. You will author the test cases, execute them, document objective evidence and pass/fail determinations, manage discrepancies to closure, and produce the validation record that demonstrates this system is fit for its intended use.

Test Case Authoring

• Author validation test cases traceable to approved User Requirements Specifications, functional specifications, and risk assessments.
• Write test scripts with unambiguous preconditions, discrete numbered steps, explicit expected results, and defined objective evidence requirements for each step.
• Ensure every requirement is covered by at least one test case and maintain the Requirements Traceability Matrix linking requirement → specification → test case → executed evidence → result.
• Develop test cases across the full validation scope:
• Installation Qualification (IQ): Environment configuration, Infrastructure provisioning, component versions, dependency verification.
• Operational Qualification (OQ): Functional verification of each specified capability against expected behavior, including boundary and negative conditions.
• Performance Qualification (PQ) / UAT: Verification that the system performs reliably under actual business use with real workflows and representative users.
• Author data integrity test cases evaluating the system against principles across the record lifecycle.

 Test Execution & Documentation

• Execute test cases and record results contemporaneously, in accordance with Good Documentation Practices.
• Capture objective evidence — screenshots, system-generated reports, database queries, log extracts — sufficient for an independent reviewer to reach the same conclusion without repeating the test.
• Record an explicit pass/fail determination for every test step and every test case, attributable and dated.
• Compile executed test packages for QA review and approval, ensuring completeness and correctness before submission.

 Validation of AI & Agentic Functionality

• Partner with the Business Analyst and Quality leadership to establish acceptance criteria for probabilistic outputs — performance thresholds against a controlled golden dataset, confidence thresholds, and defined system behavior below those thresholds — and document how pass/fail is determined against them.
• Validate the golden dataset and evaluation harness themselves. If evaluation results constitute validation evidence, the dataset provenance, labeling methodology, SME qualification, and evaluation tooling must all be qualified and under change control.
• Design and execute test cases verifying that human-in-the-loop controls function as specified: that outputs requiring review cannot bypass it, that reviewer identity and decision are captured in the audit trail, and that overrides are recorded.
• Verify traceability of AI output — that any given result can be traced to its source records, model version, prompt or configuration version, and data snapshot.
• Define the revalidation approach for model version changes, including the regression evaluation required before a new model version may be promoted, and support the associated change control.

 Environment & Infrastructure Qualification

• Qualify development, test, and production environments, including verification that test environments are representative of production for validation purposes.
• Verify configuration management and that infrastructure-as-code deployments produce the qualified configuration reproducibly.
• Review and approve automated test evidence generated through CI/CD pipelines, including qualification of the automation tooling itself where its output is relied upon as validation evidence.

 Quality System & Governance

• Ensure all validation activity conforms to company SOPs, and internal AI governance policy.
• Contribute to risk assessments determining validation rigor proportional to patient safety, product quality, and data integrity impact.
• Review and approve change controls affecting validated state; determine revalidation scope.
• Maintain the validation package in an inspection-ready condition at all times.
• Support internal audits and health authority inspections — present the validation approach, walk inspectors through evidence, and respond to observations.

 Required Qualifications

• Bachelor''s degree in Life Sciences, Computer Science, Engineering, or a related field.
• 7+ years in computer system validation or software quality assurance within a GxP-regulated environment.
• Demonstrated experience authoring and executing IQ/OQ/PQ protocols and producing validation summary reports for enterprise systems.
• Direct experience with Good Documentation Practices and contemporaneous evidence capture.
• Demonstrated experience managing test discrepancies and deviations through investigation to documented closure.
• Experience supporting internal audits or regulatory inspections, including direct interaction with auditors or investigators.
• Exceptional written precision. Validation documentation is read by people looking for gaps, and ambiguity is a finding.
• Authorized to work in the United States without current or future sponsorship.