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AI Quality Assurance Engineer
1 month ago
About Datacom
With over 6,200 people, and centres of technical excellence spread across Australasia, Datacom is committed to developing and growing its capability across Asia. Founded in 1965 in New Zealand, and with over 2 decades of operating in Malaysia and the Philippines, Datacom has experienced exponential growth. Through all this, Datacom has maintained high levels of profitability with a track record of delivering innovative, cost-effective digital and technology solutions, all delivered by dynamic teams spread across various locations. Our people are the best in their fields - smart, passionate, and dedicated to providing exceptional IT services to our customers. This makes for a rewarding and fast-paced place to work.
Our purpose
Here at Datacom we connect people and technology in order to solve challenges, create opportunities and discover new possibilities for the communities we live in.
Position Overview
We are seeking an enthusiastic and innovative AI Quality Assurance (QA) Engineer who is passionate about the latest developments in AI and machine learning. This role will focus on creating AI-driven QA solutions, adopting cutting-edge AI advancements, and ensuring the quality and reliability of AI-based systems. With 4-5 years of experience in test automation across a wide range of tools, the ideal candidate will be proactive in adopting new technologies, ensuring the robustness and accuracy of AI models, and driving the quality agenda within the organization.
Key Responsibilities
- AI QA Solutions Development: Design, develop, and implement advanced AI-powered QA automation solutions for testing AI-based applications, models, and systems. Use innovative techniques to identify performance bottlenecks, inconsistencies, and model biases.
- Test Automation & Frameworks: Build and maintain automated test frameworks for AI systems using a wide variety of automation tools (e.g., Selenium, Appium, pytest, or custom-built AI testing tools). Ensure that test automation strategies are scalable, reusable, and aligned with industry best practices.
- AI Model Testing: Perform functional and non-functional testing of AI models to assess accuracy, bias, scalability, and robustness. Conduct regression testing and ensure that AI models behave as expected across different scenarios.
- Adopt AI Innovations: Stay up to date with the latest AI advancements and research. Actively explore and adopt emerging AI technologies (e.g., GPT models, neural networks, reinforcement learning) to continuously improve the quality assurance process.
- Cloud and On-Premise AI Services: Test and validate AI solutions hosted on both cloud platforms (e.g., AWS, Azure, Google Cloud) and on-premise AI services. Ensure that the AI models perform optimally across both environments.
- Cross-functional Collaboration: Work closely with AI developers, data scientists, and product teams to identify testing requirements, troubleshoot issues, and improve overall AI system quality. Provide feedback and insights on AI model design from a QA perspective.
- Continuous Improvement: Implement and continuously improve testing methodologies for AI systems. Drive automation efficiency and effectiveness, ensuring that testing is not only comprehensive but also aligned with fast-paced AI development cycles.
- Quality Metrics & Reporting: Establish key quality metrics for AI systems, track progress, and report on the effectiveness of AI testing strategies. Maintain clear, concise, and actionable documentation and reports on test outcomes, issues found, and resolutions.
Requirements
Skills & Qualifications:
- Experience: 4-5 years of experience in test automation, with a strong focus on AI and machine learning-based applications and systems. Proven expertise in building automated test frameworks for complex systems.
- Technical Skills: Proficient in programming languages such as Python, C#, Java, or JavaScript. Hands-on experience with automation tools like Selenium, Appium, Pytest, or other testing frameworks. Experience working with cloud-based AI services (e.g., AWS AI/ML, Azure Cognitive Services, Google Cloud AI) as well as on-premise AI solutions. Knowledge of version control systems (e.g., Git) and CI/CD pipelines for automated testing.
- AI/ML Knowledge: Basic understanding of AI/ML concepts, including data preprocessing, model training, evaluation metrics, and model fairness. Familiarity with testing methodologies for AI systems, including model validation and performance testing.
- Analytical & Problem-Solving Skills: Strong analytical skills to assess AI system performance, debug issues, and design solutions to improve test coverage and efficiency.
- Innovation and Learning: Demonstrated ability to stay current with AI industry trends and developments. Ability to quickly adapt to new tools, technologies, and methodologies.
- Collaboration & Communication: Excellent teamwork and communication skills. Able to work effectively with cross-functional teams and explain complex technical concepts to both technical and non-technical stakeholders.
Preferred Qualifications:
- Experience with on-premise AI services and managing AI deployments in hybrid environments.
- Familiarity with AI ethics, fairness, and bias testing methodologies.
- Experience in test-driven development (TDD) or behavior-driven development (BDD) in AI projects.
Why join us here at Datacom?
- Be part of a dynamic, forward-thinking team that is passionate about pushing the boundaries of AI technology.
- Work in an environment that fosters innovation and continuous learning.
- Opportunity to lead the way in AI QA, making a direct impact on the quality of cutting-edge AI products.
- Competitive salary and benefits package.
If you're a proactive, tech-savvy AI QA engineer with a strong automation background and a passion for staying ahead of AI developments, we'd love to hear from you
Seniority level- Mid-Senior level
- Full-time
- Quality Assurance
- Non-profit Organizations and Primary and Secondary Education
