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Job Description & How to Apply Below
Role Name:
Senior QE Automation Engineer (AI Solutions)
Location:
Toronto / 4 days a week on-site, F2F interview required
Position Overview We are seeking an experienced Senior QE Automation Engineer to join our AI Development Lifecycle AIDLC team This role focuses on ensuring quality and reliability of AIML systems through comprehensive test automation strategies specialized AI model validation and robust quality engineering practices
Key Responsibilities Develop and maintain automated testing frameworks for AIML applications and pipelines
Create comprehensive test automation strategies covering unit integration system and endtoend testing
Implement continuous testing practices within CICD pipelines for AI model deployment
Develop automated tests for model training inference and monitoring systems
AIML Quality Assurance Validate AI model performance accuracy bias fairness and robustness
Design test cases for model drift detection and data quality validation
Implement automated testing for model versioning and AB testing scenarios
Conduct performance and load testing for ML inference endpoints
Validate data pipelines feature engineering and ETL processes
Tools Infrastructure Build and maintain test infrastructure for AI workloads
Integrate testing tools with MLOps platforms
Implement monitoring and observability for test automation systems
Manage test data and synthetic data generation for AI testing
Required Qualifications Technical Skills 5 years of experience in QA automation engineering
2 years of handson experience testing AIML systems or data intensive applications
Expertise in test automation frameworks Pytest Selenium Playwright Cypress or similar
Experience with API testing tools Postman REST Assured or similar
Proficiency with CICD tools Jenkins Git Lab CI Git Hub Actions
Strong understanding of ML concepts model training evaluation metrics inference feature engineering
AI/ML Testing Experience Experience testing machine learning models classification regression NLP
Knowledge of model evaluation metrics accuracy precision recall etc
Understanding of data quality testing and validation techniques
Familiarity with ML frameworks
Experience with model monitoring and observability tools
Infrastructure Cloud
Experience with cloud platforms AWS Lambda Azure ML
Knowledge of containerization Docker Kubernetes
Understanding of distributed systems and microservices architecture
Experience with version control systems Git and collaborative development workflows
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Position Requirements
10+ Years
work experience
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