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
- Test Automation Strategy
- Develop and maintain automated testing frameworks for AIML applications and pipelines
- Create comprehensive test automation strategies covering unit integration system and end-to-end 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 hands-on experience testing AIML systems or data intensive applications
- Strong programming skills in Python required and familiarity with other languages Java JavaScript Typescript
- 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 GitLab CI GitHub Actions
- Strong understanding of ML concepts model training evaluation metrics inference feature engineering
AIML 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
- API Framework
- FastAPI
- LLM Orchestration
- LangGraph
- Knowledge Graph
- Neo4j
- Vector Store
- pgvector
