Data Annotater
Transcribing video actions into time stamped texts and files.
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Senior AI Trainer & Data Quality Analyst – Remote (Lead annotation and evaluation). Brings 8+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Scale AI and Other. Education includes Azure AI Engineer Associate, Microsoft (2025) and Master of Business Administration, University of Nairobi (2014). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and RLHF.
Transcribing video actions into time stamped texts and files.
Performed AI training work for response review and reasoning refinement as part of AI model improvement efforts. Reviewed model outputs and applied feedback to support better alignment with expected behavior and reasoning quality. Contributed to the training workflow by evaluating responses against quality and policy expectations.• Training and review of AI model responses• Refinement of model reasoning as part of RLHF-style improvement• Quality evaluation and response scoring• Participation in iterative training workflows
Led annotation and evaluation of high-volume multilingual AI datasets to improve model accuracy and ensure output quality. Conducted linguistic quality assurance, semantic analysis, and enforced labeling guidelines to maintain consistency across examples. Mentored junior annotators on error detection and prompt evaluation to sustain performance under tight deadlines.• High-volume multilingual dataset annotation and evaluation• Linguistic quality assurance (LQA), semantic analysis, and guideline enforcement• Mentoring and training for junior annotators on best practices• Delivered consistent high-quality outputs within deadlines
Verbatim Captioning: Transcribing spoken dialogue into time-stamped text files. This trains voice assistants, meeting transcription software, and automated subtitling algorithms.
Masters, Cybersecurity
Bachelor of Science, Computer Science
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