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V7 Labs
Appen
Data Annotation Tech
Labelbox
Mercor
OneForma
Telus
Text
Image
Video
Fine-tuning
Bounding Box
Question Answering
Evaluation/Rating
Data Collection
Computer Programming/Coding
Prompt + Response Writing (SFT)
RLHF
Text Summarization
Text Generation
Classification
Segmentation
Point/Key Point
Entity (NER) Classification
Polygon
Object Detection
Polyline
CuboidI have experience working with data-driven projects involving data preparation, analysis, quality assessment, and machine learning workflows. With a Master’s degree in Computer Science and a background in machine learning, cybersecurity, and data engineering, I have developed strong skills in handling large datasets, applying structured guidelines, identifying inconsistencies, and ensuring data accuracy. My experience with Python, machine learning algorithms, and data processing techniques has strengthened my ability to understand how high-quality labeled data supports AI model training, evaluation, and performance improvement. What sets me apart is my combination of technical knowledge and attention to detail. I understand the importance of accurate annotation, consistency, and documentation in AI training data pipelines. Through my academic and technical projects involving machine learning models, data preprocessing, and model evaluation, I have gained practical experience in preparing and validating data for intelligent systems. I am highly organized, adaptable, and capable of working independently or collaborating with data scientists and engineers to deliver reliable, high-quality annotation results.
Computer Code ProgrammingFine-tuningThe initiative supported the training and assessment of Large Language Models (LLMs) through a focus on AI-generated code and technical material. The work involved evaluating code for accuracy, performance, and adherence to practical, industry-standard engineering practices, along with developing coding questions modeled on real job-related scenarios.
AudioEvaluation/RatingThe project was aimed to strengthen how well an AI model handles extended dialogue — specifically its capacity to track context across many exchanges, stay aligned with prior instructions, and deliver responses that remain both useful and coherent as a conversation unfolds.
AudioEvaluation/RatingThe objective was to evaluate Large Language Models (LLMs) in realistic usage scenarios and extended conversational contexts to identify performance limitations, uncover recurring failure patterns, and determine opportunities for enhancing model accuracy, reliability, and overall effectiveness.
Computer Code ProgrammingPrompt + Response Writing (SFT)The project involved creating a golden solution and evaluating model performance by designing challenging engineering tasks that expose limitations in current Large Language Models (LLMs). The objective was to develop benchmark test cases that existing models would struggle to solve, allowing for accurate assessment of their reasoning, problem-solving capabilities, and technical performance.
ImageEvaluation/RatingThe project involved reviewing and validating annotated subsets from a publicly available dataset containing a large collection of visual data, including images paired with corresponding contextual information and question-answer (QA) pairs. The primary objective was to enhance dataset quality by carefully evaluating annotations, verifying the accuracy and relevance of QA pairs, and curating a reliable, high-quality labeled dataset with improved confidence for AI model training and evaluation.
Doctor of Philosophy, Computer Science
Master of Science, Computer Science
SOX Compliance Auditor
Cybersecurity Analyst
