Oluwole P.

Key Skills

Software

Toloka

Roboflow

Mindrift

OneForma

Top Subject Matter

LLM quality

research evaluation

and supervised annotation for AI alignment

Top Data Types

Text

Document

Image

Top Task Types

Prompt + Response Writing (SFT)

Bounding Box

Text Generation

RLHF

Classification

Data Collection

Evaluation/Rating

Object Detection

Freelancer Overview

AI Research & Data Annotation Contributor | Outlier (AI Quality & Research Division). Brings 2+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Internal and Proprietary Tooling. Education includes Bachelor of Engineering, Federal University Oye-Ekiti (FUOYE) (2020). AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Prompt + Response Writing (SFT).

Labeling Experience

AI Research & Data Annotation Contributor | Outlier (AI Quality & Research Division)

Text

Reviewed and scored LLM-generated responses using multi-criteria rubrics focused on factual accuracy, logical coherence, instruction-following, and tone. Performed ongoing quality assurance across high-volume daily batches while documenting evidence for accuracy and guideline compliance. Supported model improvement by translating evaluation outcomes into actionable feedback for downstream work. • LLM output evaluation against rubric criteria (accuracy, coherence, instructions, tone) • Fact-checking with multi-source web research and credibility signaling • Model error analysis with structured reports and recurring failure pattern identification • Data annotation/classification across text and web content categories per project guidelines

2026 - Present

AI-Assisted Product Research Contributor | Miventy (Marketplace Platform)

TextPrompt + Response Writing (SFT)

Contributed to AI-assisted product and market research by using prompt-driven workflows to generate structured insights and platform content drafts. Integrated human-in-the-loop style review by synthesizing multi-round client feedback into revision briefs that emulate preference-based alignment cycles. Validated outputs via scenario-based testing to improve consistency and reduce errors in real-world service descriptions. • Generated and refined prompt outputs for competitive intelligence and product insights • Structured content creation for catalog logic, service flow descriptions, and onboarding copy • Information architecture validation using edge-case scenario testing • Stakeholder feedback integration into iterative revision briefs and evaluation loops

2025 - 2025

Education

Work History