Test Lead with AI Skills

Key Responsibilities

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:

• Lead on-site/client UAT sessions for Professional Services deployments.

• Ensure solutions meet client SLAs (e.g., >95% extraction accuracy).

Required Skills & Experience

• 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.

Preferred Qualifications

• 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).