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Leke-Oduoye Erioluwa

AI Data Trainer & Prompt Engineer

Nigeria flagVictoria Island, Lagos, Nigeria
$30.00/hrExpert

Key Skills

Software

No software listed

Top Subject Matter

Large Language Models
Prompt Engineering
Human Preference Data

Top Data Types

TextText
DocumentDocument

Top Task Types

Prompt Response Writing SFT
Classification

Freelancer Overview

AI Data Trainer & Prompt Engineer. Brings 6+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Internal, Proprietary Tooling, and Hugging Face Transformers. Education includes Bachelor of Science, University of Ibadan (2025) and Certificate, New Horizon (2025). AI-training focus includes data types such as Text and labeling workflows including Prompt + Response Writing (SFT), Evaluation, and Rating.

ExpertEnglish

Labeling Experience

AI Data Trainer & Prompt Engineer

TextPrompt Response Writing SFT
As an AI Data Trainer & Prompt Engineer, I designed, tested, and refined thousands of prompts for large language models, including evaluating and rating responses. I curated and annotated structured training datasets for supervised fine-tuning and RLHF, labeling intent and writing preference data for advanced LLM applications. I collaborated on defining annotation guidelines and automated data cleaning and quality-scoring processes.• Designed prompts for GPT-4, Claude, and Llama models and evaluated outputs for quality and safety • Labeled intents, ranked model outputs, and wrote human-preference data for SFT and RLHF pipelines • Automated annotation batch cleanup and scoring using Python, boosting review efficiency by 50% • Supported guideline development for labeling across coding, instruction-following, and reasoning domains.

As an AI Data Trainer & Prompt Engineer, I designed, tested, and refined thousands of prompts for large language models, including evaluating and rating responses. I curated and annotated structured training datasets for supervised fine-tuning and RLHF, labeling intent and writing preference data for advanced LLM applications. I collaborated on defining annotation guidelines and automated data cleaning and quality-scoring processes.• Designed prompts for GPT-4, Claude, and Llama models and evaluated outputs for quality and safety • Labeled intents, ranked model outputs, and wrote human-preference data for SFT and RLHF pipelines • Automated annotation batch cleanup and scoring using Python, boosting review efficiency by 50% • Supported guideline development for labeling across coding, instruction-following, and reasoning domains.

2025 - Present

LLM Prompt Evaluation & Dataset Curation Tool Developer

Text
I developed and utilized a Streamlit-based tool for large language model (LLM) prompt evaluation and structured dataset curation. I batch-tested prompts, scored responses, and exported ranked datasets for use in fine-tuning and RLHF pipelines. I managed annotation workflows, tracked reviewer agreement, and ensured label quality for model training data.• Built a rubric-driven prompt evaluation tool integrated with OpenAI API and Hugging Face • Exported structured response rankings in JSONL format for downstream LLM training workflows • Monitored inter-annotator agreement and flagged low-confidence labels with Python • Facilitated dataset readiness for preference modeling and human alignment tasks

I developed and utilized a Streamlit-based tool for large language model (LLM) prompt evaluation and structured dataset curation. I batch-tested prompts, scored responses, and exported ranked datasets for use in fine-tuning and RLHF pipelines. I managed annotation workflows, tracked reviewer agreement, and ensured label quality for model training data.• Built a rubric-driven prompt evaluation tool integrated with OpenAI API and Hugging Face • Exported structured response rankings in JSONL format for downstream LLM training workflows • Monitored inter-annotator agreement and flagged low-confidence labels with Python • Facilitated dataset readiness for preference modeling and human alignment tasks

2026 - 2026

Text Classification & Sentiment Analysis Pipeline Developer

TextClassification
I constructed and fine-tuned a text classification and sentiment analysis pipeline for customer feedback. I labeled and prepared a custom dataset of support tickets for intent and sentiment classification, then used these labeled data to train and evaluate DistilBERT and baseline models. I enabled deployment of the trained inference pipeline as a REST API for customer support automation.• Labeled 5,000 support tickets into intent and sentiment categories • Fine-tuned a DistilBERT model with the labeled text dataset using Hugging Face Trainer • Compared and validated model performance to baseline ML classifiers • Streamlined API deployment for labeled NLP pipelines in production customer support

I constructed and fine-tuned a text classification and sentiment analysis pipeline for customer feedback. I labeled and prepared a custom dataset of support tickets for intent and sentiment classification, then used these labeled data to train and evaluate DistilBERT and baseline models. I enabled deployment of the trained inference pipeline as a REST API for customer support automation.• Labeled 5,000 support tickets into intent and sentiment categories • Fine-tuned a DistilBERT model with the labeled text dataset using Hugging Face Trainer • Compared and validated model performance to baseline ML classifiers • Streamlined API deployment for labeled NLP pipelines in production customer support

2025 - 2025

AI & Embedded Systems Engineer (Data Labeling)

TextClassification
In this embedded systems engineering role, I collected, cleaned, and labeled multi-sensor IoT time-series datasets for predictive maintenance and anomaly detection use cases. I prepared training datasets from raw sensor streams, handled missing values, and manually annotated patterns corresponding to equipment failure or normal operation. I built and tested sensor-based classification models for real-world deployment.• Labeled time-series sensor data (moisture, temperature, motion, voltage) for predictive ML tasks • Annotated raw IoT sensor streams with event or equipment state labels • Validated and cleaned data using Python (pandas, NumPy) before model training • Facilitated ML inference on edge devices via labeled and preprocessed data pipelines

In this embedded systems engineering role, I collected, cleaned, and labeled multi-sensor IoT time-series datasets for predictive maintenance and anomaly detection use cases. I prepared training datasets from raw sensor streams, handled missing values, and manually annotated patterns corresponding to equipment failure or normal operation. I built and tested sensor-based classification models for real-world deployment.• Labeled time-series sensor data (moisture, temperature, motion, voltage) for predictive ML tasks • Annotated raw IoT sensor streams with event or equipment state labels • Validated and cleaned data using Python (pandas, NumPy) before model training • Facilitated ML inference on edge devices via labeled and preprocessed data pipelines

2021 - 2024

Education

N

New Horizon

Certificate, Full-Stack Software Development

Certificate
2025 - 2025
U

University of Ibadan

Bachelor of Science, Electrical and Electronic Engineering

Bachelor of Science
2018 - 2025

Work History

F

FBIS Technologies

Software & AI Systems Engineer

Victoria Island, Lagos
2025 - Present
R

Rajtronixs Technologies

AI & Embedded Systems Engineer

Sango, Ibadan
2021 - 2024