Danielle Mota Calazans

Detail-oriented AI Quality & LLM Evaluation Specialist with experience reviewing AI-generated outputs, data collection and validation of datasets, and improving training data quality for large-scale generative AI systems. Skilled in data validation, prompt evaluation, error detection, model response analysis, and identifying edge cases and safety risks across multilingual datasets. Experienced in coordinating with external vendors and leading calibration sessions to align teams on quality standards, improve consistency, and enhance data reliability.

AI Quality & LLM Evaluation Specialist (Senior AI Data Annotation Specialist) at Telus Digital (2025-07 – Present)

Conduct quality assurance reviews of AI-generated datasets and model outputs, ensuring alignment with annotation guidelines and performance standards.

• Conduct quality assurance reviews of AI-generated datasets and model outputs, ensuring alignment with annotation guidelines and performance standards.
• Evaluate LLM-generated responses, identifying inconsistencies, edge cases, and potential safety issues impacting model reliability.
• Perform data validation and quality checks to maintain accuracy and consistency across multilingual datasets.
• Collaborate with research and cross-functional teams to analyze AI system behavior and support improvements in LLM training data.
• Lead weekly calibration sessions with external vendors and labeling teams, aligning stakeholders on quality standards, resolving ambiguities, and improving annotation consistency.
• Contribute to improving AI model performance by identifying data inconsistencies, refining annotation guidelines, and enhancing dataset quality.

AI Data Annotation Specialist at Telus Digital (2024-07 – 2025-07)

Identified potential quality issues in advertising templates and AI-generated outputs.

• Identified potential quality issues in advertising templates and AI-generated outputs.
• Conducted independent research to analyze AI system performance.
• Analyzed datasets to detect patterns and trends affecting data quality.
• Collaborated with R&D teams in experiments aimed at improving Large Language Model (LLM) training.

Freelancer Personalized Internet Ads Assessor PT-BR at Telus Digital (2023-03 – 2025-04)

Evaluate online advertisements for relevance, quality, and compliance with search queries.

• Evaluate online advertisements for relevance, quality, and compliance with search queries.
• Review ad content for grammar, tone, and cultural appropriateness.
• Provide structured feedback on search engine results and advertising placements.
• Evaluated linguistic quality, tone, and contextual accuracy of AI-generated outputs across multiple languages.

Freelancer Social Media Evaluator at Appen (2021-05 – 2022-10)

Evaluated content relevance and quality across well-known social media platforms.

• Evaluated content relevance and quality across well-known social media platforms.
• Categorized social media posts according to predefined taxonomies.
• Assessed advertising relevance to improve algorithmic recommendations.

Bachelor in Economics – Faculdades Metropolitanas Unidas (2020)