Akwara Emmanuel

Key Skills

Software

Other

Labelbox

SuperAnnotate

Top Subject Matter

Legal Services & Contract Review

Medical Domain Expertise

Academic Transcription

Top Data Types

Audio

Text

Image

Document

Top Task Types

Transcription

Entity Ner Classification

Bounding Box

Prompt Response Writing SFT

RLHF

Segmentation

Freelancer Overview

Audio Transcriber & Annotator. Brings 6+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Other, Labelbox, and SuperAnnotate. Education includes Doctor of Philosophy, University of Amsterdam (2016) and Doctor of Philosophy, University of Hamburg (2012). AI-training focus includes data types such as Audio, Text, and Image and labeling workflows including Transcription, Entity (NER) Classification, and Bounding Box.

Labeling Experience

AI Code Evaluator & Programming Annotator

Other

I reviewed, labeled, and evaluated code outputs, including Python and SQL, for accuracy in data annotation and programming-focused projects. I assessed AI-generated coding solutions, tested for logical correctness, and provided structured feedback. My input directly influenced dataset quality and model retraining. • Evaluated solver and verifier code project outputs. • Identified errors and inconsistencies in AI-generated code. • Rated and edited programming solutions for annotation consistency. • Provided comprehensive feedback to improve AI coding assistants.

I reviewed, labeled, and evaluated code outputs, including Python and SQL, for accuracy in data annotation and programming-focused projects. I assessed AI-generated coding solutions, tested for logical correctness, and provided structured feedback. My input directly influenced dataset quality and model retraining. • Evaluated solver and verifier code project outputs. • Identified errors and inconsistencies in AI-generated code. • Rated and edited programming solutions for annotation consistency. • Provided comprehensive feedback to improve AI coding assistants.

2022 - Present

AI Prompt Engineer / Conversation Designer

OtherTextPrompt Response Writing SFT

I created and refined prompt-response pairs to optimize AI outputs and improve conversational relevance. I developed challenging, professional-level prompts for AI models, helping to reduce bias and hallucinations. My work contributed directly to supervised fine-tuning in large language models. • Crafted safe, accurate, and diverse prompt-response pairs. • Evaluated and rated AI-generated completions for quality. • Identified and documented edge cases and model biases. • Enhanced model robustness through adversarial prompt design.

I created and refined prompt-response pairs to optimize AI outputs and improve conversational relevance. I developed challenging, professional-level prompts for AI models, helping to reduce bias and hallucinations. My work contributed directly to supervised fine-tuning in large language models. • Crafted safe, accurate, and diverse prompt-response pairs. • Evaluated and rated AI-generated completions for quality. • Identified and documented edge cases and model biases. • Enhanced model robustness through adversarial prompt design.

2021 - Present

Image Segmentation Annotator

SuperannotateImageSegmentation

I used pixel-level segmentation techniques to annotate images for advanced AI vision projects, including medical, geospatial, or automotive datasets. I used platforms such as Labelbox and SuperAnnotate to ensure precision and consistency. This work was critical for the deployment and fine-tuning of AI models. • Segmented images for tasks like medical diagnosis and mapping. • Applied quality assurance checks on large volumetric datasets. • Annotated geospatial and satellite images for land cover classification. • Facilitated model training on segmented image data.

I used pixel-level segmentation techniques to annotate images for advanced AI vision projects, including medical, geospatial, or automotive datasets. I used platforms such as Labelbox and SuperAnnotate to ensure precision and consistency. This work was critical for the deployment and fine-tuning of AI models. • Segmented images for tasks like medical diagnosis and mapping. • Applied quality assurance checks on large volumetric datasets. • Annotated geospatial and satellite images for land cover classification. • Facilitated model training on segmented image data.

2020 - Present

RLHF Annotator / Red Teaming Specialist

OtherTextRLHF

I performed reinforcement learning from human feedback (RLHF) by ranking and rating AI-generated responses for safety, factuality, and contextual relevance. I engaged in red-teaming exercises to expose vulnerabilities and biases in large models. My contributions directly improved model alignment with intended behavior and safety requirements. • Rated AI outputs for quality, safety, and utility. • Intentionally identified and reported hallucinations or biases. • Participated in adversarial testing (red teaming). • Enhanced LLM alignment through detailed evaluation and annotation.

I performed reinforcement learning from human feedback (RLHF) by ranking and rating AI-generated responses for safety, factuality, and contextual relevance. I engaged in red-teaming exercises to expose vulnerabilities and biases in large models. My contributions directly improved model alignment with intended behavior and safety requirements. • Rated AI outputs for quality, safety, and utility. • Intentionally identified and reported hallucinations or biases. • Participated in adversarial testing (red teaming). • Enhanced LLM alignment through detailed evaluation and annotation.

2019 - Present

Text Annotator (NER, Sentiment)

OtherTextEntity Ner Classification

I categorized and labeled customer reviews by sentiment, performing named entity recognition and entity extraction on varied datasets. I used advanced annotation platforms and NLP pipelines to structure and annotate textual data. These annotations drove model training and improved downstream performance. • Found and classified named entities including names, dates, and locations. • Performed sentiment analysis and entity recognition for AI systems. • Labeled datasets using annotation guidelines for high consistency. • Supported training of NLP models for sentiment and entity extraction.

I categorized and labeled customer reviews by sentiment, performing named entity recognition and entity extraction on varied datasets. I used advanced annotation platforms and NLP pipelines to structure and annotate textual data. These annotations drove model training and improved downstream performance. • Found and classified named entities including names, dates, and locations. • Performed sentiment analysis and entity recognition for AI systems. • Labeled datasets using annotation guidelines for high consistency. • Supported training of NLP models for sentiment and entity extraction.

2017 - Present

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Education

Work History