Praise H.

Key Skills

Software

Remotasks

Appen

Top Subject Matter

AI model evaluation and annotation for LLM training (text and conversational data)

Conversational/voice AI agent evaluation and prompt dataset creation support

Legal Services & Contract Review

Top Data Types

Text

Audio

Document

Top Task Types

RLHF

Freelancer Overview

AI Data Trainer & Data Annotation Specialist (Freelance / Multiple Platforms). Brings 3+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Remotasks, Appen, and AirTM. Education includes Bachelor of Technology, Ladoke Akintola University of Technology (LAUTECH). AI-training focus includes data types such as Text and Audio and labeling workflows including RLHF, Evaluation, and Rating.

Labeling Experience

AI Voice Agent Developer & Automation Specialist (Freelance)

Audio

AI Voice Agent Developer and Automation Specialist building, configuring, and debugging AI voice agents while evaluating output quality in real business workflows. Designed and optimized system prompts for receptionist, appointment-booking, and mortgage-broker assistants, then tested instruction-following behavior and documented edge-case failures. Integrated agents with CRM and telephony/automation tools and produced structured logs that support model improvement workflows. • Configured voice AI agents using Vapi, Retell AI, and Bland AI • Evaluated hallucinations and off-policy responses in deployed agent sessions • Built prompt variations to improve response accuracy for specific clients • Integrated with GoHighLevel, Twilio/Telnyx, and Make.com/n8n for training-data-friendly logging

2024 - 2025

AI Data Trainer & Data Annotation Specialist (Freelance / Multiple Platforms)

RemotasksTextRLHF

AI Data Trainer and Annotation Specialist completing 500+ labeling and evaluation tasks for text, audio, and conversational AI datasets used in model training pipelines. Assessed AI-generated outputs for quality, factuality, helpfulness, and safety compliance using structured rubrics and RLHF-style feedback collection. Applied response ranking methods (SBS/side-by-side) to support reinforcement learning loops for large language models. • Labeled and evaluated customer service, finance, legal, and real-estate prompts/responses • Performed prompt-response ranking (SBS) for RLHF guidance • Conducted red-teaming and adversarial prompt testing to expose failure modes • Maintained high annotation consistency (reported 95%+ inter-annotator agreement)

2023 - 2024

Education

Work History