
Maxiel N
AI Data Annotation Specialist and Quality Assurance
Habilidades

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Experiencia laboral
AI Data Annotation Specialist
Avala AI • Tiempo parcial
Apr 2026 - Jun 2026 • 2 mos
Annotated and validated complex datasets for machine learning models and artificial intelligence applications. Key Responsibilities & Achievements: • Executed high-precision data annotation, categorization, and quality validation across large-scale datasets. • Ensured 100% compliance with strict client project guidelines, edge-case protocols, and accuracy standards. • Supported data preparation pipelines through systematic data verification, error identification, and quality control. • Maintained high productivity and data throughput while consistently delivering high-accuracy labels. • Collaborated remotely with cross-functional project teams to refine annotation workflows and maintain data consistency.
Senior AI Data Annotation Specialist & Quality Analyst
Sama • Tiempo completo
Mar 2024 - Mar 2026 • 2 yrs
Lead quality analyst ensuring high accuracy and reliability for machine learning and artificial intelligence datasets. Promoted from AI Data Annotator based on exceptional performance metrics. Key Responsibilities & Achievements: • Conducted comprehensive QA audits across image, video, text, audio, and LiDAR annotation projects to maintain strict data standards. • Identified annotation errors, edge cases, and pattern inconsistencies, implementing corrective actions across workflows. • Mentored, coached, and onboarded annotation team members to boost overall project precision and output. • Performed dataset validation and final quality checks prior to client delivery. • Applied expertise in computer vision (bounding boxes, polygon, segmentation) and natural language processing (NLP).
Data Annotator
CloudFactory • Tiempo completo
Jul 2017 - Dec 2018 • 1 yr 5 mos
Annotated image, video, and text data for machine learning model training across various industries. Key Responsibilities & Achievements: • Applied precision annotation guidelines across computer vision and data classification projects. • Utilized industry-standard labeling tools to tag, segment, and categorize visual data. • Maintained high accuracy and strong attention to detail in target-driven environments. • Ensured secure handling and confidentiality of sensitive project datasets.