QA Automation Engineer in AI

**Hard Skills:**

- AI/ML Model Testing & Validation

- Test Automation Frameworks (Selenium, Cypress, Playwright)

- API Testing (Postman, REST-assured, Swagger)

- CI/CD Tools (Jenkins, GitHub Actions, GitLab CI)

- Programming Languages (Python, JavaScript, Java)

- Data Validation & Integrity Checks for AI Data Pipelines

- TensorFlow, PyTorch Integration Testing

- Test Data Generation & Management

- Performance & Scalability Testing

**Soft Skills:**

- Critical Thinking & Problem Solving

- Agile & Scrum Methodologies

- Effective Communication & Documentation

- Analytical Mindset for AI Data Flows

- Continuous Learning & Adaptability

- Stakeholder Collaboration

*Lead QA Automation Engineer | InnovAI Labs | San Francisco, CA*

June 2022 – Present

- Led the design and implementation of an AI-focused automation framework, increasing test coverage of AI models and data pipelines by 40%.

- Developed custom Python scripts to validate model predictions against ground truth datasets, reducing regression testing time by 30%.

- Integrated ML model validation into CI/CD pipelines using Jenkins, enabling rapid feedback and deployment cycles.

- Collaborated with Data Scientists to refine testing strategies for explainability and bias detection in AI models.

- Mentored junior QA engineers on AI-specific testing methodologies and tools.

*QA Automation Engineer | TechNova Solutions | Austin, TX*

January 2020 – May 2022

- Automated end-to-end testing for a SaaS platform utilizing real-time AI analytics, improving defect detection rates for AI components from 65% to 90%.

- Created reusable test automation scripts using Cypress and Python, and orchestrated cross-browser testing suites that ensured compatibility across major platforms.

- Conducted API testing and validation for microservices architecture supporting AI data ingestion and processing workflows.

- Implemented performance testing strategies that identified bottlenecks in AI data processing pipelines, leading to targeted optimizations.

*Software QA Analyst | DataFlow Inc. | New York, NY*

August 2017 – December 2019

- Performed manual and automated testing of data processing modules used in training AI algorithms, ensuring data quality and pipeline integrity.

- Developed test plans for cloud-based AI services, ensuring compliance with security and performance standards.

- Coordinated with development teams to deploy updates seamlessly, reducing rollback incidents by 15%.

**Bachelor of Science in Computer Science**

New York University | 2013 – 2017

- Certified AI & ML Testing Specialist (AI Testing Institute, 2024)

- ISTQB Certified Tester — Advanced Level (2019)

- Jenkins Certified Engineer (2021)

**Bias Detection & Mitigation Toolkit for AI Models**

Built an automated testing suite that assessed models for bias using synthetic data generation, resulting in a 25% improvement in model fairness metrics during deployment.

**AI Data Pipeline Validation Framework**

Led development of a comprehensive validation framework that checked data quality, consistency, and integrity across multiple AI data flows, drastically reducing runtime errors.

- Automation Frameworks: Selenium, Cypress, Playwright, Appium

- Languages: Python, Java, JavaScript, Bash

- Testing Platforms: JUnit, TestNG, pytest

- CI/CD: Jenkins, GitHub Actions, GitLab CI/CD

- AI/ML Libraries: TensorFlow, PyTorch, scikit-learn

- Cloud & Containerization: AWS, Docker, Kubernetes

- Monitoring & Logging: Grafana, ELK Stack

- English (Fluent)

- Spanish (Professional Working Proficiency)