Hi, I’m Emmanuel Lewis Turikumwe, a data annotation and AI training specialist based in Kigali, Rwanda. I design and audit structured datasets for ML models using tools like CVAT and SQL, with a focus on image, video, text, audio, and SQL data. I enjoy building robust data workflows, enforcing guidelines, and ensuring high-quality annotations across distributed teams.
Beyond annotation, I assist in preparing data for model fine-tuning, creating dashboards, and generating research insights. I hold an MPhil in Intellectual Property and a BSc in Civil Engineering, and I communicate in English (Fluent), French (Intermediate), and Spanish (Basic). I thrive in remote settings and am comfortable coordinating with international stakeholders.
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On the LUMA Project, I worked on high-precision multimodal data annotation and evaluation tasks aimed at improving the performance of advanced AI models. My responsibilities included annotating and reviewing video content according to strict guidelines, with a strong focus on visual accuracy, temporal consistency, camera motion, scene structure, and object interactions.
I ensured that annotations were detailed, logically coherent, and aligned with model training requirements, while consistently identifying visual artifacts, motion inconsistencies, rendering errors, and prompt deviations. The role required strong attention to detail, adherence to quality standards, and the ability to provide clear, structured justifications that supported accurate AI evaluation and model refinement.
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