Conduct routine quality verification activities across raw materials, in-process production and finished goods to ensure compliance with specification.
Perform sampling, testing, inspections, sensory evaluations and product measurements in accordance with established quality requirements.
Support food safety monitoring through pre-operational checks, environmental testing, equipment verification and calibration activities.
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Product Inspection: Conduct thorough visual and physical inspections of incoming raw materials, in-process production components, and final products against engineering drawings and specifications.
Measurement & Testing: Utilize precision measuring instruments, including calipers, micrometers, height gauges, and specialized testing equipment to verify dimensional accuracy and product functionality.
Documentation: Accurately record all inspection data, test results, and daily logs in our quality management system.
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To participate and uphold the safety and security policy and procedures within the engineering CAMO department in line with airworthiness requirements and the corporate Safety objective related to CAMO SPI and SPT
Assist in organising and implementing the approved Audit program to ensure FY CAMO's continuous compliance with regulatory and company requirements.
Support the management of audit report findings, including tracking and implementing corrective and preventive actions.
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AI-Powered Testing Support: Leverage AI-powered testing tools (e.g., test case generation, visual regression detection, anomaly detection) to increase test coverage and reduce manual effort.
QA for AI Modules: Collaborate with AI/data teams to validate AI outputs (e.g., recommendation engines, predictive analytics, LLM features) by designing test cases for accuracy, bias detection, edge cases, and failure modes.
BI Dashboard Validation: Verify data accuracy and consistency in Power BI dashboards by comparing against source data, backend queries, and expected business logic.
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AI-Powered Testing Support: Leverage AI-powered testing tools (e.g., test case generation, visual regression detection, anomaly detection) to increase test coverage and reduce manual effort.
QA for AI Modules: Collaborate with AI/data teams to validate AI outputs (e.g., recommendation engines, predictive analytics, LLM features) by designing test cases for accuracy, bias detection, edge cases, and failure modes.
BI Dashboard Validation: Verify data accuracy and consistency in Power BI dashboards by comparing against source data, backend queries, and expected business logic.
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Assist the Head in developing and implementing the University’s annual audit plan, including audit planning, fieldwork, working papers, findings and audit reports.
Maintain proper documentation and records of audit assignments, findings and related reports to ensure effective tracking and compliance.
Liaise with Heads of Departments, Schools, Business Support Units, staff and relevant external parties on audit, operational and administrative matters.
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As a QA Team Lead you will design and build systems that embed quality into the development lifecycle rather than treating it as a final step. This is a hands-on leadership role working closely with product, design, and engineering to define quality ownership across squads while contributing directly through automation, tooling, and observability. Over time you will help establish and grow a scalable quality engineering function that supports the organisation as it scales.
Perform laboratory testing for incoming raw materials, packaging materials, in-process samples, stability samples, and finished products to ensure compliance with internal specifications and regulatory requirements.
Ensure laboratory equipment, tools, and instruments are routinely calibrated, maintained, and in validated condition.
Accurately document all test results in compliance with GDP (Good Documentation Practices) and maintain comprehensive test records.
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AI-Powered Testing Support: Leverage AI-powered testing tools (e.g., test case generation, visual regression detection, anomaly detection) to increase test coverage and reduce manual effort.
QA for AI Modules: Collaborate with AI/data teams to validate AI outputs (e.g., recommendation engines, predictive analytics, LLM features) by designing test cases for accuracy, bias detection, edge cases, and failure modes.
BI Dashboard Validation: Verify data accuracy and consistency in Power BI dashboards by comparing against source data, backend queries, and expected business logic.
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