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Idowu Ademola

Idowu Ademola

AI Training/LLM Fine-Tuning Lead

Nigeria flaglagos, Nigeria
$16.00/hrExpertData Annotation TechLabel StudioAppen

Key Skills

Software

Data Annotation TechData Annotation Tech
Label StudioLabel Studio
AppenAppen
OneFormaOneForma
Don't disclose

Top Subject Matter

Audio DSP programming
code LLMs
Document AI

Top Data Types

DocumentDocument
AudioAudio

Top Task Types

Fine-tuningFine-tuning
Entity (NER) ClassificationEntity (NER) Classification
Data CollectionData Collection

Freelancer Overview

AI Training/LLM Fine-Tuning Lead. Brings 13+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Azure Form Recognizer. Education includes Master of Science, Carnegie Mellon University (2014) and Bachelor of Science, University of California, Berkeley (2012). AI-training focus includes data types such as Computer Code, Programming, and Document and labeling workflows including Fine-tuning, Entity (NER) Classification, and Data Collection.

ExpertEnglish

Labeling Experience

Document and Email Entity Extraction and Labeling Engineer

DocumentEntity Ner Classification
Built an end-to-end automated data extraction engine for document and email datasets, leveraging Azure Form Recognizer, AWS Textract, and custom NLP for entity recognition and field extraction. Labeled and validated structured and semi-structured documents to create ground truth for downstream AI models. Processed and managed over 2 million document samples per month for continuous model improvement and production inference accuracy tracking. • Engineered data annotation and extraction workflows for scale. • Created labeled datasets for invoice, contract, and email fields. • Used Azure Form Recognizer and AWS Textract for semi-automatic annotation. • Performed manual entity correction and QA for model validation.

Built an end-to-end automated data extraction engine for document and email datasets, leveraging Azure Form Recognizer, AWS Textract, and custom NLP for entity recognition and field extraction. Labeled and validated structured and semi-structured documents to create ground truth for downstream AI models. Processed and managed over 2 million document samples per month for continuous model improvement and production inference accuracy tracking. • Engineered data annotation and extraction workflows for scale. • Created labeled datasets for invoice, contract, and email fields. • Used Azure Form Recognizer and AWS Textract for semi-automatic annotation. • Performed manual entity correction and QA for model validation.

2022 - Present

AI Training/LLM Fine-Tuning Lead

Fine Tuning
Curated and pre-processed a 42,000+ sample dataset of JUCE/C++ Audio DSP instruction code and supervised model fine-tuning using QLoRA workflows. Developed automated quality scoring scripts to ensure dataset accuracy and consistency before deep learning pipeline ingestion. Led open and proprietary training runs on the OpenTrainAI platform resulting in an 18% improvement on HumanEval code benchmarks over baseline LLMs. • Led data curation and validation for AI model training. • Designed and implemented machine learning pipelines for custom LLM fine-tuning. • Leveraged QLoRA and industry-standard tools for supervised instruction-based code dataset labeling. • Validated outputs via HumanEval and deployed model to production for enterprise use.

Curated and pre-processed a 42,000+ sample dataset of JUCE/C++ Audio DSP instruction code and supervised model fine-tuning using QLoRA workflows. Developed automated quality scoring scripts to ensure dataset accuracy and consistency before deep learning pipeline ingestion. Led open and proprietary training runs on the OpenTrainAI platform resulting in an 18% improvement on HumanEval code benchmarks over baseline LLMs. • Led data curation and validation for AI model training. • Designed and implemented machine learning pipelines for custom LLM fine-tuning. • Leveraged QLoRA and industry-standard tools for supervised instruction-based code dataset labeling. • Validated outputs via HumanEval and deployed model to production for enterprise use.

2022 - Present

Code Dataset Curator and Labeling Specialist

Data Collection
Curated a labeled dataset of 15,000 JUCE/C++ DSP code samples for use in supervised audio AI model development. Automated code labeling and scoring processes, significantly reducing manual curation time and improving dataset consistency. Provided essential ground truth data for LLM code model training and audio plugin AI features. • Catalogued and labeled source code for supervised learning tasks. • Automated dataset quality scoring and filtering routines. • Supported AI team with clean, high-quality code annotation. • Supplied labeled code to LLM and audio DSP model fine-tuning initiatives.

Curated a labeled dataset of 15,000 JUCE/C++ DSP code samples for use in supervised audio AI model development. Automated code labeling and scoring processes, significantly reducing manual curation time and improving dataset consistency. Provided essential ground truth data for LLM code model training and audio plugin AI features. • Catalogued and labeled source code for supervised learning tasks. • Automated dataset quality scoring and filtering routines. • Supported AI team with clean, high-quality code annotation. • Supplied labeled code to LLM and audio DSP model fine-tuning initiatives.

2016 - 2019

Education

C

Carnegie Mellon University

Master of Science, Artificial Intelligence and Machine Learning

Master of Science
2012 - 2014
U

University of California, Berkeley

Bachelor of Science, Computer Science and Data Engineering

Bachelor of Science
2008 - 2012

Work History

O

OpenTrainAI

Senior AI Infrastructure Automation Engineer

San Francisco
2022 - Present
N

Nexus Intelligence Corp.

Lead Data Scientist & Automation Engineer

Austin
2019 - 2022