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Chidinma Miracle

Chidinma Miracle

AI Trainer & Data Annotation Specialist (Text RLHF)

Nigeria flagPort Harcourt, Nigeria
$15.00/hrIntermediateAppenLabel StudioOneforma

Key Skills

Software

AppenAppen
Label StudioLabel Studio
OneFormaOneForma

Top Subject Matter

Stem Domain Expertise
Reasoning Domain Expertise
general knowledge

Top Data Types

TextText
ImageImage
VideoVideo
DocumentDocument

Top Task Types

RLHF
Classification
Action Recognition

Freelancer Overview

AI Trainer & Data Annotation Specialist (Text RLHF). Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include RLHF platforms, Atlas Capture, and Internal. Education includes Bachelor of Science, Abia State University Uturu (2016). AI-training focus includes data types such as Text, Image, and Video and labeling workflows including RLHF, Classification, and Action Recognition.

IntermediateEnglishIgbo

Labeling Experience

AI Reasoning Evaluation — Self-Directed

Text
I independently evaluated AI-generated reasoning outputs across mathematics, logic, and STEM, using structured rubrics to benchmark model performance. My workflow included prompt design, error documentation, and thorough response quality assessment for continuous LLM improvement. I provided detailed feedback to identify reasoning failures and logic issues for model developers. • Developed and refined custom evaluation rubrics for mathematical and logical assessment. • Benchmarked language model reasoning across multiple prompt scenarios and difficulty levels. • Documented error types and annotated logical inconsistencies for QA reporting. • Provided strategic feedback to inform iterative tuning and research directions.

I independently evaluated AI-generated reasoning outputs across mathematics, logic, and STEM, using structured rubrics to benchmark model performance. My workflow included prompt design, error documentation, and thorough response quality assessment for continuous LLM improvement. I provided detailed feedback to identify reasoning failures and logic issues for model developers. • Developed and refined custom evaluation rubrics for mathematical and logical assessment. • Benchmarked language model reasoning across multiple prompt scenarios and difficulty levels. • Documented error types and annotated logical inconsistencies for QA reporting. • Provided strategic feedback to inform iterative tuning and research directions.

2024 - Present

AI Content Evaluator & Quality Reviewer

Text
I systematically rated and critiqued AI-generated text outputs for accuracy, clarity, logic, and tone using self-developed evaluation rubrics. My self-directed projects mirrored professional AI trainer workflows, emphasizing factuality review, failure mode analysis, and rubric development for response quality. Results directly supported benchmarking and iterative improvement of language models across technical and instructional content. • Designed independent comparison frameworks for prompt-response assessment. • Produced structured written reports highlighting strengths, gaps, and compliance issues in model outputs. • Analyzed common failure cases like factual drift and instruction non-compliance in AI-generated text. • Documented error patterns for logic, mathematical, and general task domains.

I systematically rated and critiqued AI-generated text outputs for accuracy, clarity, logic, and tone using self-developed evaluation rubrics. My self-directed projects mirrored professional AI trainer workflows, emphasizing factuality review, failure mode analysis, and rubric development for response quality. Results directly supported benchmarking and iterative improvement of language models across technical and instructional content. • Designed independent comparison frameworks for prompt-response assessment. • Produced structured written reports highlighting strengths, gaps, and compliance issues in model outputs. • Analyzed common failure cases like factual drift and instruction non-compliance in AI-generated text. • Documented error patterns for logic, mathematical, and general task domains.

2024 - Present

Video & Image Annotation Project (Video)

VideoAction Recognition
I classified and labeled actions and events in short video clips for AI training, focusing on consistent, detailed annotation across multiple content domains. Rigorous compliance with category definitions and edge case escalation were key to maintaining dataset integrity. My work strengthened action recognition models by delivering comprehensive labeled examples and minimizing annotation drift. • Annotated events, movements, and sequences in videos according to strict guidelines. • Applied robust self-check processes to ensure frame-to-frame consistency and accuracy. • Engaged in inter-annotator alignment and feedback sessions to calibrate results. • Supported QA review by proactively flagging ambiguous or challenging video cases.

I classified and labeled actions and events in short video clips for AI training, focusing on consistent, detailed annotation across multiple content domains. Rigorous compliance with category definitions and edge case escalation were key to maintaining dataset integrity. My work strengthened action recognition models by delivering comprehensive labeled examples and minimizing annotation drift. • Annotated events, movements, and sequences in videos according to strict guidelines. • Applied robust self-check processes to ensure frame-to-frame consistency and accuracy. • Engaged in inter-annotator alignment and feedback sessions to calibrate results. • Supported QA review by proactively flagging ambiguous or challenging video cases.

2024 - Present

Video & Image Annotation Project

ImageClassification
I annotated images for computer vision datasets, applying detailed classification and object recognition standards. The tasks required strict adherence to annotation guidelines to ensure training data quality for AI models. My annotation outputs contributed to high inter-annotator consistency and robust dataset development for vision pipelines. • Utilized labeling tools to classify objects, actions, and contexts in static images. • Followed platform-specific instructions to ensure precise label placement and metadata enrichment. • Conducted self-review of submitted labels to maintain quality and consistency. • Collaborated asynchronously within distributed annotation teams to align on ambiguous cases.

I annotated images for computer vision datasets, applying detailed classification and object recognition standards. The tasks required strict adherence to annotation guidelines to ensure training data quality for AI models. My annotation outputs contributed to high inter-annotator consistency and robust dataset development for vision pipelines. • Utilized labeling tools to classify objects, actions, and contexts in static images. • Followed platform-specific instructions to ensure precise label placement and metadata enrichment. • Conducted self-review of submitted labels to maintain quality and consistency. • Collaborated asynchronously within distributed annotation teams to align on ambiguous cases.

2024 - Present

AI Trainer & Data Annotation Specialist (Text RLHF)

TextRLHF
I completed RLHF tasks, evaluating and ranking AI model responses for helpfulness, correctness, safety, and instruction compliance. My responsibilities included prompt and response annotation, factuality judgments, quality assurance, and feedback provision for fine-tuning language models. I ensured compliance with evaluation rubrics and flagged inconsistent or unsafe outputs during structured review cycles. • Rated, ranked, and annotated AI text outputs across STEM, reasoning, and general knowledge domains. • Conducted prompt engineering, crafted evaluation rubrics, and provided structured written feedback for model improvement. • Identified data inconsistencies, hallucinations, and edge cases requiring escalation during dataset QA workflows. • Achieved perfect onboarding assessment scores through precise guideline adherence and consistent annotation quality.

I completed RLHF tasks, evaluating and ranking AI model responses for helpfulness, correctness, safety, and instruction compliance. My responsibilities included prompt and response annotation, factuality judgments, quality assurance, and feedback provision for fine-tuning language models. I ensured compliance with evaluation rubrics and flagged inconsistent or unsafe outputs during structured review cycles. • Rated, ranked, and annotated AI text outputs across STEM, reasoning, and general knowledge domains. • Conducted prompt engineering, crafted evaluation rubrics, and provided structured written feedback for model improvement. • Identified data inconsistencies, hallucinations, and edge cases requiring escalation during dataset QA workflows. • Achieved perfect onboarding assessment scores through precise guideline adherence and consistent annotation quality.

2024 - Present

Education

A

Abia State University Uturu

Bachelor of Science, Mathematics

Bachelor of Science
2013 - 2016

Work History

A

Access Bank PLC

Agent Field Officer

Port Harcourt
2021 - 2023