AI QA Engineer

at  Compliance Risks

ABOUT US:

Compliance & Risks is the leading provider of product and regulatory compliance SaaS solutions. The combination of our enterprise-grade platform, C2P, extensive global regulatory content, and a team of subject matter experts enables global companies across multiple sectors (consumer electronics, household appliances, apparel, medical devices, and more) to build safe, sustainable products in a world full of change.
The company serves 300+ global enterprise customers, including GE, Google, Nike, Amazon, Ikea, Bose, Tesla, Vaillant, Unisys, Samsung and Fujitsu.

REQUIREMENTS:

• Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related field, with a focus on artificial intelligence or machine learning.
• 2+ years of experience in quality assurance/testing of AI-based applications or machine learning models, preferably in a production environment.
• Strong understanding of AI concepts and techniques, including neural networks, deep learning, reinforcement learning, and natural language processing.
• Proficiency in programming languages such as Python for scripting, test automation, and data manipulation.
• Experience with AI/ML frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, or similar.
• Familiarity with testing methodologies and tools specific to AI, such as MLflow, TensorFlow Extended (TFX), or cloud-based AI services (e.g., AWS SageMaker, Google AI Platform).
• Solid analytical and problem-solving skills, with a keen attention to detail and the ability to investigate complex issues.
• Excellent communication skills, with the ability to articulate technical concepts and findings to diverse stakeholders.
• Proven ability to work both independently and collaboratively in a dynamic, cross-functional team environment.
• Passion for innovation, continuous learning, and contributing to the advancement of AI technologies.

Preferred Qualifications:

• Experience in deploying and testing AI models in production environments, including monitoring model performance and scalability.
• Knowledge of ethical considerations and biases in AI systems, with a focus on ensuring fairness, transparency, and accountability in AI applications.
• Familiarity with big data technologies and platforms (e.g., Apache Hadoop, Spark) for managing and processing large datasets.
• Previous exposure to specific AI domains such as computer vision, speech recognition, recommendation systems, or autonomous systems.

PURPOSE:

We are seeking a skilled and proactive AI Quality Assurance Engineer to join our team, focusing on ensuring the quality and reliability of our AI projects. This role is instrumental in validating the performance and accuracy of AI models and algorithms across various domains and applications.

RESPONSIBILITIES:

• Develop comprehensive test plans and strategies for AI projects, encompassing functional testing, performance testing, and edge case validation.
• Collaborate closely with data scientists, machine learning engineers, and software developers to understand AI project requirements and design effective test cases.
• Execute both manual and automated tests to evaluate AI models, including input-output validation, data integrity checks, and performance benchmarks.
• Identify and prioritize risks associated with AI projects and create mitigation strategies to address potential issues.
• Implement and maintain automated testing frameworks and tools tailored for AI testing, leveraging technologies such as TensorFlow, PyTorch, or MLflow.
• Investigate and troubleshoot AI model failures or unexpected behaviors, employing debugging techniques and data analysis to identify root causes.
• Document testing procedures, methodologies, and outcomes to facilitate knowledge sharing and continuous improvement of QA processes.
• Stay current with advancements in AI technologies, tools, and best practices, and apply this knowledge to enhance AI testing capabilities.
• Collaborate with cross-functional teams to ensure AI solutions meet business requirements, performance goals, and ethical standards.