Al-Ameen A.

Key Skills

Software

Don't disclose

Top Subject Matter

LLM evaluation

prompt engineering

and rubric-based dataset/model assessment

Top Data Types

Text

Image

Audio

Document

Top Task Types

Classification

Transcription

RLHF

Entity (NER) Classification

Freelancer Overview

LLM Evaluation & Prompt Engineering (Independent AI Trainer & Evaluator). Brings 6+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose. Education includes Bachelor of Engineering, Federal University of Technology Akure (FUTA) (2027). AI-training focus includes data types such as Text, Image, and Audio and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

LLM Evaluation & Prompt Engineering (

TextRLHFEntity (NER) Classification

No description provided.

2024 - Present

Generative AI Content Review (Independent AI Trainer & Evaluator)

Don't discloseText

Reviewed generative AI content for coherence, grammatical accuracy, readability, and alignment with user intent using predefined evaluation guidelines. Benchmarked and assessed large language model performance and contributed structured assessments to LLM evaluation and benchmarking initiatives. Provided quality-focused review outputs to support continuous model improvement. • Applied evaluation guidelines to score readability, grammar, and coherence • Verified alignment with user intent and task requirements • Contributed structured assessments for benchmarking initiatives • Supported LLM performance evaluation through consistent review work

2024 - Present

LLM Evaluation & Prompt Engineering (Independent AI Trainer & Evaluator)

Don't discloseText

Evaluated LLM-generated responses for accuracy, instruction compliance, reasoning quality, and factual consistency using structured rubric-based scoring. Compared and ranked model outputs to support RLHF and supervised fine-tuning workflows while identifying hallucinations, safety concerns, and policy violations. Delivered calibrated, detailed feedback and engineered prompts to stress-test model capabilities and surface edge cases. • Assessed response correctness, reasoning depth, and adherence to user instructions • Ranked competing model outputs to guide optimization and training • Identified hallucinations, logical errors, and safety/policy issues • Produced actionable feedback and prompt improvements for model iteration

2024 - Present

Search Relevance & Information Retrieval (Independent AI Trainer & Evaluator)

Don't discloseText

Assessed search engine outputs for relevance to user queries, information accuracy, and overall result quality. Conducted multi-source web research to verify facts and support projects aimed at improving search relevance. Applied structured rating frameworks to evaluate how well content satisfies user intent. • Rated search results by relevance, accuracy, and quality • Performed multi-source fact verification to reduce misinformation • Evaluated user intent satisfaction using rating frameworks • Supported search quality improvement through research findings

2023 - Present

AI Data Annotation & Quality Evaluation (

ImageRLHFEntity (NER) Classification

No description provided.

2022 - 2024

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Education

Work History