Test Lead with AI Skills1

Test Strategy & Leadership:

Define end-to-end test plans for AI solutions (OCR, NLP, document automation) including functional, regression, UAT, and performance testing.
Lead a team of QA engineers in Agile/Scrum environments.

AI Product Testing:

Validate OCR accuracy (Google/Azure OCR), AI model outputs (Layout Parsing, data extraction), and NLP logic across diverse document types (invoices, contracts).
Design tests for edge cases: low-quality scans, handwritten text, multi-language docs.

Automation & Tooling:

Develop/maintain automated test scripts (Selenium, Cypress, pytest) for UI, API, and data validation.
Integrate testing into CI/CD pipelines (Azure DevOps/Jenkins).

Quality Advocacy:

Collaborate with AI engineers and BAs to identify risks, document defects, and ensure resolution.
Report on test metrics (defect density, false positives, model drift).

Client-Focused Validation:

Experience:
- 8+ years in software testing, including 3+ years testing AI/ML products (OCR, NLP, computer vision).
- Proven experience as a Test Lead managing teams (5+ members).
Technical Expertise:
- Manual Testing: Deep understanding of AI testing nuances (training data bias, model drift, confidence scores).
- Test Automation: Proficiency in Python/Java, Selenium/Cypress, and API testing (Postman/RestAssured).
- AI Tools: Hands-on experience with Azure AI, Google Vision OCR, or similar.
- Databases: SQL/NoSQL (MongoDB) validation for data pipelines.
Process & Methodology:
- Agile/Scrum, test planning, defect tracking (JIRA), and performance testing (JMeter/Locust).
- Knowledge of MLOps/testing practices for AI models.

Experience with document-intensive domains (P2P, AP, insurance).
Certifications: ISTQB Advanced, AWS/Azure QA, or AI testing certifications.
Familiarity with GenAI testing (LLM validation, hallucination checks).
Knowledge of containerization (Docker/Kubernetes).