Ebrahim Ahmed

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Image

Text

Top Task Types

Audio Recording

Classification

Emotion Recognition

Evaluation Rating

Prompt Response Writing SFT

Freelancer Overview

I have hands-on experience in training AI systems, particularly chatbots, by crafting high-quality prompts, evaluating their responses, and providing detailed corrections to enhance their conversational accuracy and relevance. This role required strong analytical thinking, attention to nuance, and a deep understanding of human communication to ensure the AI aligned with user expectations and context. In addition to natural language training, I have contributed to image labeling projects, where precision and consistency were critical in annotating visual data for computer vision tasks. I also participated in audio data collection by recording my voice to support speech recognition model improvements. My multi-modal data training background—spanning text, image, and audio—demonstrates versatility and a strong commitment to building better AI systems.

Labeling Experience

AI training

Scale AIAudioData Collection

In this project, I contributed to the development of speech recognition and voice-based AI systems by recording a diverse set of scripted audio samples. The goal was to improve the model’s ability to understand different accents, tones, and speech patterns in natural language. I followed detailed recording guidelines to ensure clarity, correct pronunciation, and consistent pacing across all submissions. The project involved capturing hundreds of high-quality voice recordings across various contexts and sentence structures. Quality measures included adherence to background noise requirements, precise script delivery, and the use of approved recording equipment. All submissions were reviewed for accuracy and clarity, playing a key role in training AI systems to better recognize and interpret human speech.

In this project, I contributed to the development of speech recognition and voice-based AI systems by recording a diverse set of scripted audio samples. The goal was to improve the model’s ability to understand different accents, tones, and speech patterns in natural language. I followed detailed recording guidelines to ensure clarity, correct pronunciation, and consistent pacing across all submissions. The project involved capturing hundreds of high-quality voice recordings across various contexts and sentence structures. Quality measures included adherence to background noise requirements, precise script delivery, and the use of approved recording equipment. All submissions were reviewed for accuracy and clarity, playing a key role in training AI systems to better recognize and interpret human speech.

2024

AI chatbot training

Scale AITextText GenerationEvaluation Rating

In my role with Outlier, I was involved in a large-scale AI chatbot training project focused on enhancing the performance, accuracy, and contextual understanding of conversational AI systems. The primary task involved evaluating chatbot responses to user-generated prompts by assessing their relevance, coherence, and helpfulness. I provided structured feedback and corrections, ensuring the model adhered to guidelines on tone, factual accuracy, safety, and user alignment. The project operated at scale, involving thousands of prompts across a wide range of topics and domains. We adhered to strict quality control measures, including detailed annotation guidelines, peer reviews, and periodic audits to ensure consistency and high annotation standards. Timeliness and accuracy were essential, and our work directly contributed to the fine-tuning and alignment of cutting-edge language models.

In my role with Outlier, I was involved in a large-scale AI chatbot training project focused on enhancing the performance, accuracy, and contextual understanding of conversational AI systems. The primary task involved evaluating chatbot responses to user-generated prompts by assessing their relevance, coherence, and helpfulness. I provided structured feedback and corrections, ensuring the model adhered to guidelines on tone, factual accuracy, safety, and user alignment. The project operated at scale, involving thousands of prompts across a wide range of topics and domains. We adhered to strict quality control measures, including detailed annotation guidelines, peer reviews, and periodic audits to ensure consistency and high annotation standards. Timeliness and accuracy were essential, and our work directly contributed to the fine-tuning and alignment of cutting-edge language models.

2024

Education

Work History