# QA Engineer - IoT, Automation, AI, Platform, System

> Optimiza · Amman, Jordan · Full-time · Posted 2025-07-17

**Workplace:** on_site

**Department:** Cloud & Digital Services (CDS)

## Description

**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

**requirements:**

\-        Bachelor’s or Master’s in Computer Science, Embedded Systems, Information Technology, or equivalent experience

\-        5–7+ 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

### **Preferred Skills**

·        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

## Benefits

Class A Medical Insurance

## Apply

[Apply at Optimiza](https://apply.workable.com/optimiza-4/j/A20167E4D2/apply)

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