Sebastine A.

Key Skills

Software

No software listed

Top Subject Matter

Structured data annotation and AI output evaluation

Data validation

annotation quality frameworks

Top Data Types

Text

Top Task Types

Data Collection

Freelancer Overview

IT Engineer & AI Data Specialist at Toontechs Services (Data labeling and AI evaluation). Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Microsoft 365. Education includes Bachelor of Science, Houdegbe North American University (2018). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Labeling Experience

IT Engineer & AI Data Specialist at Toontechs Services (Data labeling and AI evaluation)

Text

Created and enforced a validation framework to improve structured data annotation accuracy across 10,000+ monthly records, reducing labeling errors. Applied systematic reasoning analysis to evaluate and annotate 500+ structured AI outputs per week, including hallucination, logic-gap, and off-task detection before delivery. Designed prompt templates and instruction-following rubrics to assess and correct AI-generated responses across 200+ daily tasks. • Validated structured outputs against quality benchmarks and QA criteria. • Measured performance via internal QA benchmarks and weekly evaluation scores. • Conducted near-zero error rate quality assurance through repeatable evaluation workflows. • Supported iterative improvement of labeling and evaluation processes for client delivery.

2023 - Present

Frontend Web Developer at 3rionics Technologies (Built annotation quality/validation systems)

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Built Excel-based validation systems and strict data entry protocols to process and structure 2,000+ data records per month with high accuracy. Developed a structured output review system to reduce client-reported data errors by 45% and adapted it into an annotation quality framework for evaluating AI-generated data across two client projects. Created domain-specific evaluation rubrics and reasoning benchmarks to train 4 engineers and reduce team error rates by 38%, supporting consistent labeling quality. • Performed data quality checks and structured review of annotated records. • Used discrepancy logs to monitor and improve labeling accuracy and consistency. • Authored training materials and rubrics for standardized evaluation. • Ensured evaluation workflows aligned with client quality requirements.

2021 - 2022

Education

Work History