QA Engineer

Location: Jordan

The Opportunity

Reporting to the Project Manager or QA Lead, you will evaluate complex, distributed systems—including IoT platforms, AI pipelines, and cloud-native architectures—to ensure robust performance, functional accuracy, security, and scalability. You will drive test automation, promote proactive QA engineering, and implement modern practices to uphold system-level reliability and integration integrity.

Key Responsibilities

Design and execute both manual and automated test cases targeting IoT devices, edge-to-cloud systems, AI models, and platform services

Influence requirements and engineering practices to reduce defect density and improve testability of AI-integrated and sensor-driven applications

Identify and troubleshoot cross-layer issues (device, edge, backend, AI inference) using systematic diagnostics and logging tools

Automate regression, integration, and system tests using Selenium, Pytest, Postman, Cucumber, or similar frameworks

Develop test frameworks supporting device emulation, sensor data simulation, and model prediction validation

Use tools such as JMeter, SOAPUI, Postman, Locust, or Gatling to assess platform performance and scalability

Perform full-stack QA—including API testing, message queue validation (MQTT/AMQP), cloud service verification, and model output analysis

Conduct non-functional testing such as latency benchmarking, fault tolerance (e.g., network disconnection), and AI model response under load

Build and maintain test automation pipelines integrated into CI/CD systems (e.g., Jenkins, GitHub Actions, GitLab CI)

Own end-to-end test efforts, including test strategy definition, execution, reporting, traceability, and feedback to development

Participate in agile ceremonies (sprint planning, retrospectives, backlog grooming) to provide test estimates and QA-focused risk assessments

Drive continuous improvement by experimenting with new QA tools for device telemetry testing, AI confidence scoring, and automation frameworks

Collaborate closely with embedded engineers, backend developers, data scientists, DevOps, and product teams to ensure system-wide quality

Comply with QHSE (Quality Health Safety and Environment), Business Continuity, Information Security, Privacy, Risk, Compliance Management and Governance of Organizations policies, procedures, plans, and related risk assessments.

Skills and Attributes for Success

Test automation coverage and reliability

Device-to-platform integration validation

Defect rates in production vs QA

Reduction in manual regression cycles

Adherence to test standards and secure testing practices

Uptime/latency impact testing of integrated AI/IoT systems

requirements:

Bachelor's or Master's in Computer Science, Embedded Systems, Information Technology, or equivalent experience

–7+ years of QA experience in software testing (manual + automation), with a focus on IoT platforms, cloud systems, or AI pipelines

Strong background in test automation with tools like Selenium, Pytest, REST-assured, or Cucumber

Hands-on experience testing distributed architectures, REST APIs, message brokers (MQTT/Kafka), and cloud-based microservices

Familiarity with embedded systems, sensor data streams, device simulation/emulation, or IoT gateways

Experience with CI/CD integration, automated test triggers, and code-to-test mapping

Good understanding of network protocols, system architecture, and cloud infrastructure (AWS/GCP/Azure)

Solid experience in SQL or NoSQL for data validation and analytics layer testing

· Exposure to AI/ML model testing, including inference behavior, response times, and false positive/negative checks

· Experience in BDD (Behavior Driven Development) for complex, domain-driven test definitions

· Knowledge of platform observability tools (Grafana, Prometheus, Kibana) to correlate test outcomes with system logs and metrics

· Experience in performance, security, and risk-based testing for connected systems

· Knowledge of test virtualization and mocking tools for sensor or AI inputs

· Excellent communication and collaboration skills, particularly in cross-disciplinary teams involving hardware, software, and data

· Fluent English and Arabic

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