Quality Assurance Engineer

• Location: Remote / Pakistan
• Department: Engineering & Product Delivery
• Employment Type: Full-Time
• Reports To: QA Lead / Engineering Manager

About AIM

AIM is a Canadian technology company that helps organizations modernize their systems through advanced API management, cloud engineering, security solutions, and full-stack software development. Our teams work across North America and globally, delivering stable, scalable, and secure digital platforms for enterprise clients.

We take pride in being hands-on, collaborative, and focused on delivering real results for our clients. As we continue to grow, we are expanding our teams across technology, operations, delivery, and business functions to support increasing client demand and drive the next stage of our growth.

Position Overview

We are seeking an Quality Assurance Engineer who combines strong software testing fundamentals with practical experience using Generative AI as part of the Software Development Lifecycle (SDLC).

This role is ideal for a QA professional who understands both traditional quality engineering principles and the evolving AI-assisted engineering landscape. You will be responsible for ensuring software quality through manual testing, automated testing, API validation, and AI-powered quality engineering practices.

The successful candidate will demonstrate the ability to leverage AI tools such as Claude, ChatGPT, Microsoft Copilot, or equivalent technologies to improve test design, accelerate automation, enhance defect discovery, and improve overall delivery quality.

Key Responsibilities

Manual & Functional Testing

Design, develop, and execute comprehensive test plans, test cases, and testing strategies.

Perform functional, regression, integration, smoke, exploratory, and user acceptance testing.

Validate requirements and ensure complete feature coverage across web and mobile applications.

Identify, document, prioritize, and track defects through resolution.

API & Backend Testing

Conduct API testing using tools such as Postman.

Validate request/response payloads, authentication mechanisms, and data integrity.

Perform database validation and backend testing using SQL queries.

Test Automation

Develop, maintain, and enhance automated test suites.

Create automation scripts using Selenium, Cypress, Playwright, or equivalent frameworks.

Support CI/CD pipelines and automated testing initiatives.

Continuously improve automation coverage and framework reliability.

AI-Assisted Quality Engineering

Utilize Claude, ChatGPT, Microsoft Copilot, Gemini, or equivalent AI technologies within the SDLC.

Generate AI-assisted test cases, test plans, and testing strategies.

Leverage AI to accelerate exploratory testing and defect identification.

Utilize AI-assisted approaches to generate automation scripts.

Perform defect triage and root cause analysis using AI-enhanced workflows.

Produce high-quality QA documentation using AI-assisted techniques.

Identify opportunities to improve engineering efficiency through responsible AI adoption.

Collaboration & Delivery

Work closely with Product Owners, Business Analysts, Developers, and DevOps teams.

Participate in sprint planning, backlog grooming, reviews, and retrospectives.

Contribute to QA best practices, standards, and process improvements.

Support release validation and production readiness activities.

Required Qualifications

Mandatory Requirements

3+ years of software QA, Quality Engineering, or Software Testing experience.

Demonstrated automated testing experience using Selenium, Cypress, Playwright, or similar frameworks.

Strong understanding of SDLC, STLC, Agile, and Scrum methodologies.

Hands-on experience testing web and/or mobile applications.

Experience with API testing using Postman or similar testing tools.

Working knowledge of SQL for backend validation and testing.

Proficiency using Jira, TestRail, Zephyr, or comparable QA platforms.

Hands-on use of Claude, ChatGPT, Microsoft Copilot, or similar Generative AI tools within the software development lifecycle.

Ability to demonstrate AI-assisted test design and automated testing techniques during the interview process.

Minimum one recognized AI certification completed before joining AIM.

Strong analytical thinking, communication skills, and attention to detail.

AI Quality Engineering Requirements

Candidates must demonstrate practical experience in:

AI-assisted test case creation.

AI-assisted test strategy development.

AI-assisted exploratory testing.

AI-assisted defect identification and analysis.

Prompt engineering for QA use cases.

AI-assisted automation script generation.

AI-powered documentation and knowledge management.

Responsible and secure use of Generative AI technologies.

Technical Skills

Quality Assurance

Manual Testing

Functional Testing

Regression Testing

Smoke Testing

Integration Testing

UAT Testing

Exploratory Testing

Defect Management

Automation

Selenium

Cypress

Playwright

TestNG

JUnit

Automation Framework Design

Tools & Platforms

Jira

TestRail

Zephyr

Postman

Azure DevOps

GitHub

Jenkins

Databases

SQL Server

PostgreSQL

MySQL

Programming & Scripting

Java

JavaScript

Python

C#

Preferred Qualifications

Experience working within CI/CD pipelines.

Experience with Azure DevOps, GitHub Actions, or Jenkins.

Experience testing cloud-native applications hosted on Azure, AWS, or GCP.

Experience testing AI-powered products, copilots, agents, or LLM-driven solutions.

Knowledge of performance testing and security testing.

Experience working with globally distributed engineering teams.

Preferred Certifications

Preferred Certification: Claude Certified Architect, Foundations

Acceptable Microsoft Alternative

Microsoft Certified: Azure AI Engineer Associate (AI-102)

Relevant Microsoft Applied Skills certifications related to AI technologies

Other recognized Microsoft AI certifications

Note: AB-730 may be considered a supplementary credential but will not be accepted as the primary technical AI qualification.

Practical Assessment

All shortlisted candidates must successfully complete a practical assessment before progressing to final interviews.

AI-Assisted QA Assessment

Candidates must complete a 45 to 60-minute practical exercise demonstrating how they would use Claude (or an equivalent Generative AI platform) to quality assure an application.

The assessment will evaluate the candidate's ability to:

Analyze application requirements.

Create structured test plans and test cases.

Identify and document defects.

Prioritize testing activities.

Utilize AI to improve testing effectiveness and productivity.

Generate automated test scripts.

Explain testing decisions and quality engineering approach.

Demonstrate practical AI-assisted testing workflows.