Validation QA Tester

Job Summary

The Validation QA Tester leads, executes, and documents end-to-end computer system validation lifecycles for clinical software systems containing embedded machine learning modules. Core daily tasks include authoring IQ/OQ/PQ protocols, tracking requirements matrices (RTM), evaluating LLM hallucination metrics using specialized toolsets, conducting drift monitoring, performing adversarial prompt-injection security tests, and preparing audit-ready packages for health authority inspections.

Required Core CSV & GxP Qualifications

• Professional Experience: 4 to 7 years of direct experience executing Computer System Validation (CSV) or Software Quality Assurance within a strictly regulated GxP environment (GLP, GCP, or GMP).
• Clinical Systems Expertise: Proven history validating core enterprise clinical trial systems, including Electronic Data Capture (EDC), Clinical Trial Management Systems (CTMS), Electronic Trial Master Files (eTMF), Interactive Response Technology (IRT/RTSM), or Electronic Patient-Reported Outcomes (ePRO/eCOA).
• Regulatory Framework Literacy: Thorough, practical working knowledge of global life sciences mandates, including FDA 21 CFR Part 11, EU Annex 11, ICH E6 (R3) guidelines, GAMP 5 risk-based approaches, and ALCOA+ data integrity principles.

Required Software Engineering & Automation Skill Stack

• Test Management Ecosystems: Deep familiarity navigating enterprise test suite environments such as Jira accompanied by Xray extensions, or Azure DevOps.
• Automation Programming & Scripting: Practical proficiency utilizing Python for data parsing and automated scripting, alongside working knowledge of relational database SQL queries.
• UI Automation Frameworks: Hands-on experience developing or executing automated test scripts using browser frameworks like Selenium or Playwright.
• Core DevOps Infrastructures: Clear structural understanding of automated Continuous Integration/Continuous Deployment (CI/CD) pipelines, Git version control strategies, and repository management.

Highly Desirable AI/ML & MLOps Exposure

• Machine Learning Frameworks: Direct exposure to or deep conceptual understanding of the Machine Learning lifecycle, specifically concerning tokenized embeddings, transformer models, and Retrieval-Augmented Generation (RAG).
• AI Evaluation Toolsets: Prior exposure to automated AI/LLM evaluation frameworks such as RAGAS, DeepEval, or Promptfoo to score accuracy and model behaviors.
• Operational MLOps Concepts: General awareness of data drift monitoring, model versioning, model registries, prompt versioning protocols, and Responsible AI safety rules.
• Emerging Guidelines: Conceptual familiarity with evolving artificial intelligence standards, including the FDA Good Machine Learning Practice (GMLP), Predetermined Change Control Plans (PCCP), and the EU AI Act.

Key Responsibilities

1. Advanced GAMP 5 Life-Cycle Validation & GxP Compliance (35%)

• Coordinate End-to-End CSV: Lead and execute comprehensive computer system validation workflows for modernized clinical software suites containing embedded algorithmic modules.
• Author Lifecycle Documentation: Produce and review essential lifecycle files, including User Requirement Specifications (URS), Functional Specifications (FS), Design Specifications (DS), and high-density Requirements Traceability Matrices (RTM).
• Design Validation Protocols: Author, execute, and record formalized Installation Qualification, Operational Qualification, and Performance Qualification (IQ/OQ/PQ) protocols.
• Conduct Risk Assessments: Apply GAMP 5 risk-based principles to build Validation Plans, Software Risk Assessments, and final Validation Summary Reports tailored for automated environments.

2. AI/ML, LLM, & RAG System Validation Architecture (30%)

• Engineer AI Evaluation Rubrics: Establish, maintain, and run evaluation testing systems designed to calculate model accuracy, system hallucination rates, and semantic alignment.
• Execute Adversarial Testing: Run proactive adversarial validation testing sessions, including simulated prompt-injection exploits, safety bias reviews, and PII leakage diagnostic checks.
• Track Model Deviations: Perform regular data drift monitoring and record structural variance reports as operational clinical trial data inputs shift over time.
• Validate AI Audit Frameworks: Ensure that automated system audit trails, Human-in-the-Loop (HITL) manual overrides, and prompt versioning control tracking systems are functioning properly.

3. Defect Management, Deviation Control, & QMS Auditing (20%)

• Administer QMS Data: Manage system defects, software deviations, Corrective and Preventive Actions (CAPA), and technical change control records within the Quality Management System.
• Enforce ALCOA+ Standards: Review software operations data continuously to guarantee that all clinical trial documentation matches ALCOA+ data integrity rules.
• Compile Audit Packages: Assemble and index high-quality validation documentation into complete, inspection-ready packages suitable for health authority review.
• Support Regulatory Inspections: Provide direct technical support and system evidence during formal regulatory audits led by international health inspectors from the FDA, EMA, or MHRA.

4. Technical Automation & DevSecOps Workflow Optimization (15%)

• Develop Automated Scripts: Build, refine, and deploy scalable automated testing routines utilizing Selenium, Playwright, or Python-driven configurations to accelerate operational throughput.
• Optimize CI/CD Integrations: Collaborate with system engineering leads to integrate compliance testing, linting protocols, and regression test suites cleanly into CI/CD pipelines.
• Oversee MLOps Registries: Monitor system interaction logs and model version registries to confirm that software updates do not alter validated baseline operational metrics.
• Champion Quality Standards: Provide continuous peer-level training to software development groups, ensuring internal agile methodologies line up perfectly with external GxP validation laws.

Core Competencies & Skills

• Elite Analytical Precision: Uncompromising attention to logical detail, ensuring absolute consistency across thousands of tracing points, requirement rows, and execution logs.
• Hybrid Regulatory Adaptability: Exceptional capability to combine traditional pharmaceutical quality rules seamlessly with rapid, non-deterministic AI behavior patterns.
• Structured Problem-Solving: A methodical approach to debugging software errors, assessing system risks, and documenting deviations without losing sight of product deadlines.
• Technical Articulation Clarity: Excellent written and verbal communication skills in English, with a proven ability to explain complex machine learning processes clearly to traditional validation auditors.

Expected Outputs & Deliverables

• Fully executed, completely traceable IQ/OQ/PQ protocol packages and cross-referenced tracing matrices.
• Comprehensive GAMP 5 Risk Assessments, Validation Master Plans, and final System Sign-off Reports.
• Specialized AI/LLM validation profiles detailing model drift metrics, prompt security testing, and hallucination scoring records.
• Defect logs, open deviation registries, CAPA files, and approved system change controls ready for internal review.

Skills Required:

• Computer / Software / It / Data