Adesina Abdulrahman

Engineered and optimized enterprise applications using C#, .NET Core, and ASP.NET, improving system performance and reducing processing time by 15%.

Developed and deployed scalable RESTful APIs with ASP.NET Web API and GraphQL, significantly enhancing system interoperability.

Optimized Oracle database queries using PL/SQL and Entity Framework, reducing query execution time by 40%.

Strengthened system reliability by implementing JWT authentication and OAuth, ensuring secure access management for critical applications.

Led the migration of internal legacy systems to a cloud-based microservices architecture, successfully reducing infrastructure costs by 20%.

Built and optimized high-quality ETL pipelines using Apache Spark, SQL, and Airflow, ensuring robust data availability for analytics.

Conducted A/B testing and statistical modeling with Python, Scipy, and Statsmodels, increasing campaign conversion rates by 18%.

Deployed AI-driven solutions on AWS (SageMaker, Lambda) and GCP AI, optimizing model inference times by 5%.

Automated data preprocessing and feature engineering with Pandas and Numpy, reducing manual effort by 25%.

Developed scalable machine learning models using Python and TensorFlow, leveraging cloud-based distributed computing for enhanced performance.

Built a Generative QA system utilizing Dense Passage Retrieval (DPR) and Retrieval-Augmented Generation (RAG) with PyTorch, demonstrating advanced NLP capabilities.

Developed an AI-based driver drowsiness detection model using computer vision and deep learning techniques, contributing to enhanced road safety.