Skills
Required
test
ai
strategy
leadership
ocr
automation
regression
testing
agile
scrum
azure
model
layout
parsing
data
extraction
logic
contracts
design
tooling
selenium
ui
api
devops
jenkins
report
metrics
drift
software
ml
vision
training
python
java
postman
sql
nosql
mongodb
planning
tracking
jira
jmeter
certifications
aws
checks
containerization
docker
kubernetes
Job
Description
Test Lead with AI Skills1 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). Show more Show less
