Ai Quality Assurance Lead

About Datacom
With over 6,200 people, and centre’s 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 expediential 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
Key Responsibilities
- Test Automation & Frameworks: Build and maintain automated test frameworks for AI systems using a wide variety of automation tools (e.g., Playwright, Appium, pytest, or custom-built AI testing tools). Ensure that test automation strategies are scalable, reusable, and aligned with industry best practices. Senior test automation coding skills in at least one of the following languages: python, typescript, java, C#.
- 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 solutions 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 solutions 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 solution 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.
- Pipeline development: Can describe and demonstrate the place and value propositions for test automation pipelines with AI solutions using PaaS (e.g. Azure DevOps, GitHub Actions).
- 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.
- Communication Skills: Ability to communicate (both written and orally) abstract concepts and value points to stakeholders in terms that can be understood and discussed.

**Requirements**:
**Skills & Qualifications**:

- Technical Skills:

- Proficient in programming languages such as Python, C#, Java, or Typescript with a strong proficiency in python.
- 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 boun