Adewunmi A.

Key Skills

Software

Toloka

Axiom AI

Google Cloud Vertex AI

HiveMind

Img Lab

Top Subject Matter

NLP / LLM training data

data quality

and evaluation

Top Data Types

Text

Document

Image

Top Task Types

Fine-tuning

Point/Key Point

Freelancer Overview

Machine Learning Engineer — AI Model Evaluation & Data Annotation (IEMA Standards Ltd, Hybrid). Brings 7+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include DataAnnotation.tech, Toloka, and OpenTrain AI. Education includes Advanced Diploma in Software Engineering, APTECH Computer Education (2013). AI-training focus includes data types such as Text and labeling workflows including Fine-tuning.

Labeling Experience

Web Applications Developer (Kaplan Energy Ltd, Remote) — AI/data training related ML pipeline work

TextFine-tuning

Worked on AI training data workflows and full-cycle machine learning pipeline delivery with an emphasis on training data QA and annotation quality. Supported model evaluation by reviewing output quality against accuracy, coherence, factual grounding, and instruction-following standards across large datasets. Contributed to prompt engineering for LLM fine-tuning use cases to improve response quality and mitigate hallucinations. • Performed or oversaw data annotation/validation/QA processes to maintain dataset quality. • Supported annotation rubric usage and documented guidelines as part of team standards. • Helped deliver improved data-driven outcomes through dataset curation and QA. • Collaborated cross-functionally in agile sprints to ship milestones on schedule.

2024 - Present

Machine Learning Engineer — AI Model Evaluation & Data Annotation (IEMA Standards Ltd, Hybrid)

TextFine-tuning

Built and maintained high-quality training datasets by performing rigorous data annotation, validation, and QA on complex NLP tasks. Conducted structured AI model evaluation cycles by assessing outputs for accuracy, coherence, factual grounding, and instruction-following alignment across large sample volumes. Engineered prompt templates to support LLM fine-tuning and reduce hallucinations in domain-specific use cases. • Achieved >95% inter-annotator agreement on complex NLP annotation tasks. • Evaluated model behavior across 10,000+ samples using defined evaluation criteria. • Improved model training efficiency by optimizing data preprocessing and feature engineering. • Authored annotation guidelines and model evaluation rubrics used as team standards.

2020 - Present

Education

Work History