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Data Annotation Tech
Appen
Remotasks
Scale AI
Toloka
Labelbox
SuperAnnotate
Text
Image
Video
Classification
Bounding Box
Text Generation
Question Answering
RLHF
Fine-tuning
Evaluation/Rating
Data Collection
Segmentation
Object Detection
Text Summarization
Transcription
Prompt + Response Writing (SFT)I have extensive experience in AI training data and data labeling, with over 5,000 completed AI evaluation and annotation tasks across platforms such as Outlier AI, DataAnnotation, Appen, and Alignerr. My work has focused on evaluating large language model (LLM) outputs for reasoning accuracy, factual correctness, safety alignment, and response quality. I have experience ranking AI-generated responses, identifying hallucinations and inconsistencies, and providing structured feedback used to improve reinforcement learning from human feedback (RLHF) pipelines. I am also experienced in prompt engineering, dataset quality assurance, and annotation workflows involving text, reasoning, and analytical tasks. What sets me apart is my strong analytical background in mathematics, combined with hands-on experience working on real-world AI training projects. I am highly detail-oriented, skilled at maintaining annotation consistency and quality standards, and comfortable working with structured data formats, including JSON. In addition to AI training experience, I have a professional background as a Data Analyst in the aviation industry, where I work with operational datasets, reporting systems, and data-driven decision-making processes. My combination of technical analysis, critical reasoning, and large-scale AI evaluation experience allows me to contribute effectively to high-quality AI training and data labeling projects.
TextFine-tuningWorked with structured annotation workflows utilizing JSON schemas for storing prompts, AI-generated responses, annotation labels, reviewer feedback, and reasoning evaluations. Ensured schema consistency and accurate metadata labeling for machine learning datasets.
TextEvaluation/RatingEvaluated AI-generated content to identify hallucinations, misleading responses, unsafe outputs, and biased reasoning patterns. Annotated responses according to safety and alignment criteria to support the development of more reliable and trustworthy AI systems.
TextData CollectionAnnotated and reviewed large-scale AI training datasets used for generative AI model fine-tuning. Performed quality assurance checks on labeled datasets, corrected inconsistencies, and ensured compliance with annotation guidelines and structured data schemas, including JSON-based workflows.
TextQuestion AnsweringDesigned and tested prompts to evaluate large language model performance across mathematics, coding, logical reasoning, and analytical tasks. Conducted comparative analysis of AI responses under different prompt structures and documented model behavior, reasoning quality, and instruction adherence.
TextEvaluation/RatingWorked on evaluating AI-generated responses for reasoning accuracy, factual correctness, safety alignment, and instruction-following quality. Ranked competing model outputs and provided structured human feedback used in reinforcement learning from human feedback (RLHF) pipelines. Identified hallucinations, inconsistencies, and logical reasoning errors in large language model outputs.
Bachelor of Science, Mathematics
Data Analyst
Web Designer
