Valentine J.

Key Skills

Software

Other

Data Annotation Tech

Google Cloud Vertex AI

Labelbox

Label Studio

Sloth

Snorkel AI

SuperAnnotate

Top Subject Matter

Software testing and quality validation for secure

large-scale web systems

Cloud security

Top Data Types

Computer Code Programming

Text

Document

Top Task Types

Prompt + Response Writing (SFT)

RLHF

Question Answering

Object Detection

Red Teaming

Data Collection

Function Calling

Evaluation/Rating

Fine-tuning

Text Summarization

Text Generation

Transcription

Classification

Entity (NER) Classification

Segmentation

Computer Programming/Coding

Point/Key Point

Freelancer Overview

Data Labeling & Annotation Specialist (Contract / Part-Time, Remote) OpenTrainAI via LivestocX Agri-Tech Inc.. Brings 3+ years of professional expert across AI training and software quality assurance. Education includes Bachelor of Science, University of Jos (2020). AI-training focus includes data types such as Text and labeling workflows including RLHF, Evaluation, and Rating.

Labeling Experience

Data Labeling & Annotation Specialist (Contract / Part-Time, Remote) — OpenTrainAI via LivestocX Agri-Tech Inc.

OtherTextRLHF

Performed structured data labeling and annotation for AI/ML training pipelines, supporting text classification, intent labeling, entity recognition, and semantic similarity scoring for agricultural and commercial datasets. Applied detailed annotation guidelines to label thousands of samples with high consistency, escalating ambiguous or edge cases to improve labeling rubrics and policy updates. Contributed to RLHF by rating and ranking AI-generated outputs for quality, factual accuracy, and alignment with task instructions. • Text classification and intent labeling across labeled datasets • Entity recognition (NER) and semantic similarity scoring • RLHF evaluation/ranking of model outputs for alignment and factuality • Dataset validation with logs and feedback documentation for model training handoffs

2024 - Present

Software Development Engineer in Test (SDET) — Barnksforte Technologies Limited

Text

Reviewed and validated large volumes of software output data for functional accuracy, data integrity, and edge-case behavior that directly supports AI data quality and annotation review. Designed structured test cases and validation rubrics to ensure consistent evaluation criteria across datasets intended for large-scale model use. Documented defects and anomalies in structured formats to maintain traceable quality records and support refinement of acceptance criteria and judgment standards. • Structured validation rubrics mirroring annotation guideline consistency checks • Edge-case identification and quality improvement workflows • Defect/anomaly documentation for traceable quality records • Collaboration to refine acceptance criteria and evaluation judgment

2023 - Present

Education

Work History