Validation QA Tester

We are seeking a Validation QA Tester with strong experience in Computer System Validation (CSV) within GxP-regulated environments, and a growing interest or exposure to AI/ML-enabled applications
.

This role sits at the intersection of quality assurance, clinical systems, and emerging AI technologies
. You will be responsible for ensuring that modern clinical trial platforms - including those powered by LLMs, RAG systems, and predictive models - are compliant, reliable, and fit for purpose in line with global regulatory standards.

• Lead and execute end-to-end CSV activities for clinical trial systems with embedded AI components
• Review and contribute to URS, FS, DS
  , and maintain Requirements Traceability Matrices (RTM)
• Author and execute IQ/OQ/PQ protocols and both manual and automated test scripts
• Develop Validation Plans, Risk Assessments, and Summary Reports using a GAMP 5 risk‑based approach
• Manage defects, deviations, CAPAs, and change controls within QMS frameworks
• Design and implement validation strategies for AI/ML features (LLMs, RAG tools, predictive models)
• Build and maintain evaluation frameworks to measure accuracy, hallucination rates, and safety
• Perform drift monitoring, adversarial testing
  , and AI risk validation (e.g., prompt injection, bias, PII leakage)
• Ensure audit trails, HITL controls, and prompt versioning are validated and maintained
• Support regulatory inspections and audits (FDA, EMA, MHRA)

• 4-7 years' experience in CSV / Validation QA in a GxP-regulated environment
• Strong background validating clinical systems (e.g., EDC, CTMS, eTMF, IRT/RTSM, ePRO/eCOA)
• Working knowledge of 21 CFR Part 11, EU Annex 11, ICH E6 (R3), GAMP 5, and ALCOA+ principles
• Exposure to AI/ML systems in regulated environments (highly desirable)
• Familiarity with emerging frameworks such as FDA GMLP, PCCP, EU AI Act

• Experience with test management tools (e.g., Jira/Xray, Azure Dev Ops)
• Automation tools such as Selenium or Playwright
• Proficiency in Python and working knowledge of SQL
• Understanding of CI/CD pipelines, Git, and version control
• Knowledge of ML lifecycle concepts
  , including LLMs, embeddings, and RAG
• Hands‑on experience with AI evaluation tools (e.g., RAGAS, Deep Eval, Promptfoo)
• Familiarity with MLOps concepts (model versioning, drift monitoring, model registries)
• Awareness of Responsible AI principles

• High‑quality validation documentation and audit‑ready packages
• Executed and traceable IQ/OQ/PQ protocols
• Comprehensive risk assessments and validation reports
• AI validation outputs
  , including drift analysis and adversarial testing reports
• Inspection‑ready deliverables supporting regulatory compliance

• Work on cutting‑edge AI‑driven clinical systems
• Be part of a forward‑thinking quality and innovation team
• Opportunity to shape AI validation practices in regulated industries
• Collaborative environment with strong regulatory and technical expertise