Shuntoria Reid

Evaluated and refined Large Language Model (LLM) responses to improve accuracy , tone, and safety , ensuring the AI aligns with human intent and factual correctness. Crafted high-quality prompts and response sets to "teach" AI models how to handle nuanced creative writing and technical problem-solving tasks. Collaborated on iterative feedback loops, identifying edge cases where the model failed and providing the necessary data corrections to prevent future errors. Classified and tagged large datasets (text, image, or audio) with high precision, directly contributing to the training of machine learning algorithms for global tech clients. Followed project guidelines to review ambiguous data, achieving over 98% accuracy . Performed regular audits of labeled data to identify patterns of inconsistency , helping the team streamline labeling instructions for better efficiency . Managed the end-to-end entry and verification of sensitive project data, ensuring 100% data integrity and compliance with RWS security standards. Streamlined data collection workflows by identifying repetitive manual tasks and suggesting more efficient input methods. Cross-referenced multi-source information to resolve discrepancies, preventing data silos and ensuring the team had access to the most current project metrics. Synthesized comprehensive annotation guidelines through systematic documentation and frequent consultation with project leads, facilitating smoother onboarding for new team members and reducing initial ramp-up time.

Performed high-volume data labeling and annotation across various datasets to improve the accuracy of machine learning models. Identified and corrected inconsistencies in complex data strings, ensuring 95%+ accuracy rates in line with project-specific guidelines. Collaborated with project leads to refine annotation rubrics, helping to streamline the data pipeline for large-scale AI training. Evaluated and rated search engine results to improve the relevance and quality of AI-driven responses. Analyzed user intent and provided detailed feedback on query-to-result mapping, directly contributing to more intuitive user experiences. Maintained a high quality-of-service score by consistently meeting strict deadlines and performance benchmarks. Performed accurate image labeling for computer vision projects, improving data quality and reducing model retraining time. Categorized visual data in detail, helping speed up object detection model development. Resolved edge cases in regular feedback loops to keep datasets accurate. Standardized annotation practices by documenting process refinements, facilitating consistent data labeling across multiple project phases.

Performed end-to-end exploratory testing on various mobile and web applications to identify functional defects before public release. Authored clear, actionable bug reports including reproduction steps, screenshots, and crash logs, helping developers quickly resolve high-priority issues.

Worked with global testing teams to verify software ran smoothly on various systems, helping prevent issues. Evaluated user interface (UI) and user experience (UX) for diverse digital products, providing feedback that improved navigation and accessibility . Executed test cases and regression suites to ensure new feature deployments didn't compromise existing site functionality . Verified localized content and features for specific geographic markets, ensuring cultural and functional relevance for global users. Tracked defect trends and identified recurring issues using data analysis, contributing to process improvements and enhanced software stability .