Nnamani M.

Key Skills

Software

Other

Top Subject Matter

NLP and AI training data (sentiment, intent, NER)

scientific/academic content domains

RLHF response evaluation (quality/safety/factuality) for AI training

Top Data Types

Text

Document

Image

Top Task Types

RLHF

Classification

Freelancer Overview

Data Annotation & Labeling (Freelance / Self-Directed) — AI Training Projects (Remote). Brings 4+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Higher National Diploma, Institute of Management and Technology (IMT), Enugu. AI-training focus includes data types such as Text and Document and labeling workflows including Entity (NER), RLHF, and Classification.

Labeling Experience

Data Annotation & Labeling (Freelance / Self-Directed) — Scientific Content Labeling (Remote)

OtherDocumentClassification

You labeled scientific and academic content with attention to subject-matter accuracy in areas such as biochemistry, environmental science, and health-related topics. You verified citation details and cross-referenced sources to ensure factual correctness for training materials. You maintained clean, consistent categorizations aligned with annotation guidance. • Scientific/academic content categorization with domain-accurate labels. • Citation data verification and source cross-referencing. • Quality-oriented verification to reduce factual errors. • Consistent application of labeling rules across technical documents.

2024 - Present

Data Annotation & Labeling (Freelance / Self-Directed) — RLHF Workflow Reviews (Remote)

OtherTextRLHF

You reviewed and categorized AI-generated responses to assess quality, safety, and factual accuracy as part of RLHF-style workflows. You used the evaluation criteria to ensure outputs met the expected standards for training and improvement cycles. You maintained consistent judgment across tasks to support reliable feedback signals. • Quality and safety evaluation of AI-generated responses. • Factual correctness verification using consistency and correctness checks. • Categorization of response quality for training workflows. • Documentation and escalation of issues when guidance was not met.

2024 - Present

Data Annotation & Labeling (Freelance / Self-Directed) — AI Training Projects (Remote)

OtherText

You labeled and annotated NLP text datasets for sentiment analysis, intent classification, and named entity recognition (NER) tasks, following provided annotation instructions. You applied consistent guideline adherence across large volumes of examples to maintain accuracy and inter-annotator agreement. You flagged mislabeled or ambiguous items for escalation and resolution to improve dataset reliability. • Sentiment labeling and intent classification for AI/ML training. • Named Entity Recognition (NER) annotation with guideline-based consistency. • Quality checks to detect inconsistencies and labeling errors. • Collaboration-oriented escalation of ambiguous or mislabeled data.

2024 - Present

Education

Work History