Fresher AI Test Engineer

Collaborate with the AI test team to execute test plans, test cases, and test scenarios specifically tailored for AI systems.

Interpret and implement quality assurance standards within the context of AI testing to ensure the accuracy, reliability, and performance of AI solutions.

Execute, maintain, and enhance Automated Test Frameworks and Scripts dedicated to testing AI components, leveraging relevant AI tools and Deep learning frameworks.

Execute manual and automated tests to validate the functionality, reliability, and performance of AI algorithms and models.

Implement benchmarking tests to evaluate the performance of AI systems against industry standards and competitors.

Accurately report and track defects and issues related to AI testing, including writing detailed benchmarking /bugs reports and verifying fixes in collaboration with the development team.

Analyse benchmarking results to identify strengths, weaknesses, and areas for improvement in AI algorithms and models.

Collect and analyse data for benchmarking AI performance metrics, contributing to the establishment and enhancement of robust reporting mechanisms.

Continuously learn and adapt to emerging technologies and advancements in AI testing methodologies.

Coordinate User Acceptance Testing (UAT) and collaborate closely with the AI development team and other stakeholders to ensure comprehensive testing coverage.

Required Skill:

Bachelor’s or master’s degree in computer science, Data Science, Machine Learning, Deep Learning, Computer Vision or related fields.

Eagerness to upgrade and develop skills in testing AI Algorithms Understanding of computer vision and deep learning algorithms from testing perspective.

Familiarity with Python, TensorFlow, Keras, PyTorch,OpenCV, pytest  and willingness to learn related packages.

Strong analytical and critical thinking.

Familiarity with Version Control Systems like Git, essential for managing AI model versions and iterations.

Must have good communication skills.

Preferred Skill:

Exposure to statistical analysis skills to evaluate model performance, validate results, and identify potential issues during AI testing.

Exposure of AI Pipelines and Continuous Integration/Continuous Deployment (CI/CD) pipelines, tailored for AI development and testing workflows.

Certification in Deep Learning or Computer Vision.

Good to have experience in computer vision and/or deep learning.

Good to have knowledge of performance / load testing.

Having experience in database testing is preferrable.

The AI Tester should have one of the following certifications.