Qudus A.

Key Skills

Software

Don't disclose

Other

Top Subject Matter

AI output evaluation

RLHF annotation

safety/bias and hallucination detection

Top Data Types

Text

Document

Top Task Types

RLHF

Classification

Data Collection

Freelancer Overview

AI Response Evaluator & Annotator (Independent/Freelance). Brings 5+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose, Google Sheets, and Other. Education includes Bachelor of Pharmacy, Lagos State University College of Medicine (2022). AI-training focus includes data types such as Text and Document and labeling workflows including RLHF, Classification, and Data Collection.

Labeling Experience

Scientific Literature Review & Data Extraction (Lagos State University College of Medicine)

OtherDocumentData Collection

Critically evaluated peer-reviewed pharmacological literature for evidence quality, methodological rigor, and data reliability using a systematic, annotation-like approach. Extracted and structured information from complex scientific texts into standardized formats with high attention to precision and consistency. Authored structured academic reports with strict formatting and citation requirements to demonstrate documentation discipline relevant to labeling and feedback tasks. • Assessed study evidence quality and reliability with a repeatable rubric • Extracted structured data from scientific documents into consistent formats • Produced formatted reports with citation rigor • Applied systematic evaluation practices analogous to annotation quality assurance

2023 - Present

AI Response Evaluator & Annotator (Independent/Freelance)

Don't discloseTextRLHF

Reviewed and labeled AI-generated text responses across diverse topics using structured rubrics to assess quality dimensions including factual accuracy, instruction adherence, helpfulness, tone, and formatting. Detected and documented hallucinations, logical inconsistencies, and unsafe or biased outputs in standardized annotation formats for downstream reinforcement learning from human feedback pipelines. Completed comparative ranking and pairwise preference-style judgments to produce fine-grained, calibrated labels with written justification. • Applied rubric-based scoring and calibration for consistent RLHF-ready annotations • Performed response ranking across multiple outputs with justification for relative quality • Ensured inter-rater reliability by following style guides and quality control checklists • Recorded safety, bias, and hallucination findings for model improvement feedback loops

2023 - Present

Text Classification & Content Labeling (Self-Directed Practice)

TextClassification

Labeled and categorized large volumes of text data using multiple annotation ontologies including sentiment, intent, topic, toxicity, and factual correctness. Built self-directed annotation workflows with templates to track decisions, edge cases, and ambiguous examples for later review. Practiced prompt construction and prompt quality assessment to evaluate whether instructions are clear and capable of eliciting well-formed model responses. • Performed multi-label text categorization (sentiment, intent, topic, toxicity, factual correctness) • Maintained structured spreadsheets for annotation tracking and reconciliation • Evaluated prompts for clarity and unambiguity • Identified and documented ambiguous cases during practice labeling

2022 - Present

Education

Work History