Adam E.

Key Skills

Software

Labelbox

Top Subject Matter

AI code generation quality assurance and evaluation

LLM reasoning and output evaluation for training data preparation

Top Data Types

Computer Code Programming

Text

Top Task Types

Entity (NER) Classification

Tracking

Classification

Freelancer Overview

AI QA Engineer (Alignerr AI). Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Bachelor of Science, British University in Egypt (2024) and Bachelor of Science, London South Bank University (2024). AI-training focus includes data types such as Computer Code, Programming, and Text and labeling workflows including Evaluation, Rating, and Tracking.

Labeling Experience

AI QA Engineer (Alignerr AI)

Reviewed and annotated AI-generated code completions across Python, JavaScript, and SQL to assess correctness and quality. Maintained high QA accuracy through weekly evaluations and provided structured feedback on logic errors, inefficiencies, and best-practice violations. Curated edge cases and examples to reduce hallucinations and support supervised fine-tuning across multiple programming and ML domains. • Annotated 2,000+ code completions for code correctness and quality signals. • Evaluated 100+ coding/ML tasks weekly with a maintained 98% QA accuracy rating. • Documented feedback for model logic errors, inefficiencies, and rule violations. • Provided curated edge cases to improve supervised fine-tuning data quality.

2024 - Present

Data Annotator

LabelboxComputer Code ProgrammingEntity (NER) Classification

-Reviewed and annotated over 2,000+ AI-generated code completions across languages such as Python, JavaScript, and SQL to improve model accuracy and adherence to best practices. – Identified and corrected logic errors and inefficiencies in hundreds of code samples, contributing to a 15-20% improvement in model correctness on internal benchmarks. – Provided structured technical feedback on AI code outputs with a 98% QA accuracy rating, ensuring consistency and reliability in training data. – Collaborated with a team of engineers and trainers to evaluate 100+ coding tasks weekly, optimizing the model’s problem- solving capabilities across real-world scenarios. – Helped reduce model hallucinations in code generation tasks by curating high-quality examples and edge cases for supervised fine-tuning – Maintained a 98% QA accuracy across weekly evaluations of 100+ coding and ML tasks. – Created structured ML training data covering Python, JS, SQL, DS/ML pipelines, and algorithmic reasoning ta

2024

AI Data Curator (Outlier AI)

TextTrackingClassification

Evaluated outputs and reasoning chains from multiple large language models for code correctness, factual accuracy, and logical consistency. Produced structured feedback and contributed labeled training/evaluation artifacts in collaboration with quality managers. Supported model improvement efforts by refining data used for downstream training and assessment. • Assessed LLM responses for correctness, factuality, and reasoning consistency. • Added structured feedback for model evaluation projects. • Collaborated with quality managers on shared evaluation and annotation work. • Contributed training data annotations to support continual model refinement.

2024 - 2025

Education

Work History