Founder · AI Research Engineer · AI Engineer
Ayodeji Mobayonle Ajayi is a founder, artificial intelligence research engineer, and AI engineer with professional experience across the United States, Europe, and Nigeria. His work focuses on production AI systems, large language model workflows, machine learning operations, intelligent document processing, enterprise automation, analytics pipelines, and cloud-based AI platforms.
He combines hands-on engineering with model evaluation, dataset improvement, product delivery, technical leadership, system deployment, and the translation of complex AI capabilities into reliable tools for teams and users.
Contributes to large language model improvement through dataset analysis, response-quality review, model-behaviour assessment, fine-tuning support, and evaluation. Work has included multi-billion-parameter model workflows and reinforcement learning processes representing more than 20,000 user preferences.
Built and scaled a cloud-based AI product across architecture, workflow logic, backend services, deployment, monitoring, product operations, and quality governance. Maintained 99.9% uptime and planned capacity for up to 5,000 concurrent users.
Designed internal automation, assistant systems, backend infrastructure, and scalable services for document lookup, support workflows, data processing, and scheduled execution.
Led production machine-learning integration, recommendation systems, predictive models, operational dashboards, and financial forecasting workflows. The work reduced post-deployment incidents and improved resource efficiency.
Developed automated document generation, institutional information retrieval, structured data pipelines, and standardized reporting workflows.
Built documentation, notification, monitoring, and operational analytics pipelines that connected machine-learning models with business processes.
Worked with Oracle databases, SQL, cloud concepts, exploratory payroll-data analysis, and enterprise data-governance practices.
Model evaluation, response quality, fine-tuning workflows, NLP, summarization, classification, named-entity recognition, and sentiment analysis.
Supervised and unsupervised learning, regression, classification, clustering, deep learning, computer vision, and predictive analytics.
MLOps, deployment validation, monitoring, backend integration, cloud AI, system performance, cost efficiency, and reliability.
Python, SQL, analytics pipelines, document processing, operational dashboards, reporting, and enterprise workflow automation.
Published in Explainable Artificial Intelligence in Medical Decision Support Systems. DOI: 10.1049/PBHE050E_ch9.
View publicationUniversity of East London
Landmark University
IBM AI Engineering Professional
IBM Data Science Professional
IBM Machine Learning, with honours
IBM Deep Learning
FinOps Certified Practitioner
Google Project Management Professional
Nigeria Association of Computing Students, Landmark University
Data Fellows