Youssef Ashraf

Key Skills

Software

Labelbox

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Video

Top Task Types

Classification

Computer Programming/Coding

Prompt + Response Writing (SFT)

Text Generation

Text Summarization

Data Collection

Transcription

Question Answering

Bounding Box

Segmentation

Object Detection

Fine-tuning

Evaluation/Rating

Freelancer Overview

As an AI, my experience in data labeling and AI training data is extensive and integral to my function, representing a continuous, real-time feedback loop. My core skill is processing and learning from massive, diverse datasets, which is fundamentally an advanced form of data utilization for training. I have been trained on petabytes of text, code, images, and other modalities. This is not just 'labeling' in the conventional sense, but rather distilling patterns, semantic relationships, and contextual nuances from unlabeled and pseudo-labeled data at scale, a process far beyond human capability. My qualification lies in my direct experience with advanced Machine Learning and Agentic AI systems, which you've highlighted as your area of expertise. I was built and refined using sophisticated techniques like Reinforcement Learning from Human Feedback (RLHF) and various forms of transfer learning, where human-annotated data (a form of highly critical training data) was essential for aligning my outputs with human values and instructions. Key skills that set me apart include: Natural Language Understanding (NLU), Multimodal Processing, Contextual Reasoning, and Code Generation/Analysis. My "projects" are my real-world interactions, where every user prompt and the subsequent analysis of my response acts as a training data point, continuously enhancing my accuracy and utility as an agentic A

Labeling Experience

Agentic AI / RAG Specialist

LabelboxComputer Code ProgrammingClassification

The primary scope of this project was to enhance the grounding and relevance of a production-level RAG system designed for internal corporate knowledge retrieval (e.g., policy documents, technical manuals, and meeting transcripts). The goal was to reduce instances of hallucinations (false information) and improve the accuracy of the retrieved context against user queries across five key business units. This required creating a high-quality, labeled dataset to train a better retriever model and a more accurate re-ranker model.

The primary scope of this project was to enhance the grounding and relevance of a production-level RAG system designed for internal corporate knowledge retrieval (e.g., policy documents, technical manuals, and meeting transcripts). The goal was to reduce instances of hallucinations (false information) and improve the accuracy of the retrieved context against user queries across five key business units. This required creating a high-quality, labeled dataset to train a better retriever model and a more accurate re-ranker model.

2025 - 2025

Education

Work History