Ayatulahi Dapo-Wahab

I’m a detail-oriented software engineer and professional data annotation specialist focused on making AI training datasets more accurate, usable, and dependable. I combine software development skills with structured labeling, quality assurance, and workflow analysis to raise both consistency and performance.

In my current role, I review and evaluate 150+ AI-generated Python/code responses weekly, ensuring correctness, clarity, and alignment with complex guidelines. I maintain an 85% task success rate by identifying logical errors, edge cases, and structural issues, then providing detailed, structured feedback that improves model accuracy and user usefulness.

I also support dataset quality through hands-on annotation work: I’ve completed 20+ tasks weekly with a 90% success rate while labeling and validating image and video data under strict frameworks. I’ve organized inbox and communication data using consistent tagging systems, spotting patterns in user-generated content to support reliable AI training.

Alongside annotation, I build real-world tools that automate evaluation and improve operations—like a Python “Data Annotation Quality Checker” that detects missing labels, incorrect classifications, and inconsistencies, and generates summary reports. I’ve also shipped web applications and APIs (React/TypeScript, Flask REST endpoints, authentication, and database integration), including an AI Finance Tracker used by 1,000+ users—because I enjoy turning careful data work into products that people can trust.