QA /Testing Professional (Commodities & AI Intelligence focus)
| Posting date: | 05 January 2026 |
|---|---|
| Salary: | £51,500 to £55,000 per year |
| Hours: | Full time |
| Closing date: | 04 February 2026 |
| Location: | Brentwood, Essex |
| Remote working: | Hybrid - work remotely up to 5 days per week |
| Company: | SSOVEREIGN SOFTWARE SERVICES LTD |
| Job type: | Permanent |
| Job reference: | SSSL_PrimeQA01 |
Summary
Job Summary :
We are seeking a detail-oriented and highly technical QA / Test Engineer to lead the quality assurance and validation efforts for Trade Product in AI, an enterprise-grade commodities trading platform. The ideal candidate will have a strong background in testing complex financial systems and AI/ML models, specifically focusing on Natural Language Machine (NLM) outputs and predictive analytics. You will be responsible for ensuring the platform’s "Intelligence Layer" provides accurate, explainable, and secure trading insights while meeting strict GCC and international regulatory standards
Key Responsibilities:
AI & NLM Validation: Design and execute specialized test cases to validate the accuracy and "explainability" of Natural Language Machine (NLM) outputs and conversational AI responses.
Functional & Trading Logic Testing: Verify complex trading engines, including commodity price forecasting models, margin optimization, and stress-testing scenarios.
Data Integrity & Ingestion QA: Ensure the reliability of diverse data ingestion streams from ERP systems, FX feeds, logistics, and market news.
Security & Compliance Testing: Conduct rigorous testing to ensure the platform adheres to ISO 27001, UAE PDPL, and other international data protection and regulatory environments.
Automation Framework Development: Build and maintain automated testing suites for end-to-end trade lifecycles, including trade approvals, contract management, and audit trail generation.
Performance & Scalability Testing: Execute load and stress tests on the cloud-native, modular architecture to ensure low-latency performance during high-volume trading.
Integration Testing: Validate seamless communication between the backend AI models, frontend dashboards, and external data sources.
UAT Leadership: Support User Acceptance Testing (UAT) during the final delivery phases, working closely with commodities traders and stakeholders .
Key Skills and Qualifications:
Education: Bachelor’s degree in Computer Science, Software Engineering, or a related technical field.
Experience: Minimum 5+ years of experience in QA/Testing, with a significant focus on Fintech, trading platforms, or AI-integrated systems.
Core Tech Stack: Proficiency in testing Python/Java-based backend services and SQL/NoSQL databases.
Testing Tools & Frameworks: * Automation: Selenium, Pytest, or Robot Framework.
AI/ML Testing: DeepEval, Giskard, or WhyLabs (for AI monitoring and bias detection).
API Testing: Postman, Rest-Assured, or SoapUI.
Performance: JMeter, K6, or Locust.
Test Management: Jira, Xray, or TestRail.
Domain Expertise: Understanding of commodities trading, risk management, and GCC-centric trade operations is highly desirable.
Analytical Mindset: Strong ability to troubleshoot complex "black box" AI models and translate technical bugs into business-impact reports.
Professional Attributes: High attention to detail, proactive approach to risk mitigation, and the ability to operate in a milestone-driven environment.
Communication: Excellent ability to collaborate with AI/ML engineers and developers to ensure "Security & Compliance" are baked into the development lifecycle.
We are seeking a detail-oriented and highly technical QA / Test Engineer to lead the quality assurance and validation efforts for Trade Product in AI, an enterprise-grade commodities trading platform. The ideal candidate will have a strong background in testing complex financial systems and AI/ML models, specifically focusing on Natural Language Machine (NLM) outputs and predictive analytics. You will be responsible for ensuring the platform’s "Intelligence Layer" provides accurate, explainable, and secure trading insights while meeting strict GCC and international regulatory standards
Key Responsibilities:
AI & NLM Validation: Design and execute specialized test cases to validate the accuracy and "explainability" of Natural Language Machine (NLM) outputs and conversational AI responses.
Functional & Trading Logic Testing: Verify complex trading engines, including commodity price forecasting models, margin optimization, and stress-testing scenarios.
Data Integrity & Ingestion QA: Ensure the reliability of diverse data ingestion streams from ERP systems, FX feeds, logistics, and market news.
Security & Compliance Testing: Conduct rigorous testing to ensure the platform adheres to ISO 27001, UAE PDPL, and other international data protection and regulatory environments.
Automation Framework Development: Build and maintain automated testing suites for end-to-end trade lifecycles, including trade approvals, contract management, and audit trail generation.
Performance & Scalability Testing: Execute load and stress tests on the cloud-native, modular architecture to ensure low-latency performance during high-volume trading.
Integration Testing: Validate seamless communication between the backend AI models, frontend dashboards, and external data sources.
UAT Leadership: Support User Acceptance Testing (UAT) during the final delivery phases, working closely with commodities traders and stakeholders .
Key Skills and Qualifications:
Education: Bachelor’s degree in Computer Science, Software Engineering, or a related technical field.
Experience: Minimum 5+ years of experience in QA/Testing, with a significant focus on Fintech, trading platforms, or AI-integrated systems.
Core Tech Stack: Proficiency in testing Python/Java-based backend services and SQL/NoSQL databases.
Testing Tools & Frameworks: * Automation: Selenium, Pytest, or Robot Framework.
AI/ML Testing: DeepEval, Giskard, or WhyLabs (for AI monitoring and bias detection).
API Testing: Postman, Rest-Assured, or SoapUI.
Performance: JMeter, K6, or Locust.
Test Management: Jira, Xray, or TestRail.
Domain Expertise: Understanding of commodities trading, risk management, and GCC-centric trade operations is highly desirable.
Analytical Mindset: Strong ability to troubleshoot complex "black box" AI models and translate technical bugs into business-impact reports.
Professional Attributes: High attention to detail, proactive approach to risk mitigation, and the ability to operate in a milestone-driven environment.
Communication: Excellent ability to collaborate with AI/ML engineers and developers to ensure "Security & Compliance" are baked into the development lifecycle.