Olumide Olowu

Architected and deployed end-to-end machine learning models and AI tools, enhancing system performance and addressing complex real-world challenges.

Designed and integrated intelligent algorithms into existing APIs, optimizing AI application functionalities and scalability.

Built and implemented automated data collection tools, significantly improving data pipeline quality and efficiency.

Conducted in-depth analysis of large datasets, extracting critical insights to improve model accuracy and inform data-driven decision-making.

Ensured adherence to ethical AI practices and data protection policies, contributing to the development of proprietary intellectual property.

Analyzed and interpreted large datasets to extract actionable insights, directly informing strategic decisions and enhancing business outcomes.

Designed and implemented robust data models, algorithms, and AI tools, solving critical business problems and boosting operational efficiency.

Developed and deployed custom AI tools, integrating them with scalable data pipelines to enable intelligent automation.

Created interactive visualizations and dashboards, effectively communicating complex data findings to diverse, non-technical stakeholders.

Ensured high data quality and maintained adherence to best practices in data management, governance, and compliance standards.

Delivered bespoke NLP and Computer Vision solutions using Python, TensorFlow, and PyTorch, implementing state-of-the-art machine learning algorithms for diverse client requirements.

Developed advanced solutions for text classification, sentiment analysis, object detection, and image segmentation, exceeding client expectations and achieving optimal model performance.

Integrated Retrieval-Augmented Generation (RAG) and fine-tuned Large Language Models (LLMs), enhancing information retrieval and delivering context-aware outputs.

Consistently delivered projects on schedule, significantly improving client satisfaction and securing repeat business opportunities.

Spearheaded the development of intelligent customer chatbots, leveraging NLP models for intent recognition, entity extraction, and sentiment analysis to enhance chatbot responsiveness and accuracy.

Led a cross-functional team in designing conversational flows and incorporating business logic, ensuring seamless user experiences.

Built and deployed machine learning models using TensorFlow, Scikit-learn, and NLTK, optimizing performance with caching and scaling strategies on AWS.

Integrated chatbot models into full-stack applications, deploying React-based UI and Flask/Django APIs on AWS EC2 with secure HTTPS/SSL communication.

Oversaw Next.js website deployment via AWS Amplify, optimizing performance with CDN caching and automatic scaling for high traffic.

Analyzed passenger data and developed a recommendation system for personalized travel offers.

Collaborated with data analysts to identify key features and segments.

Utilized machine learning techniques to create a recommendation engine that enhanced customer experience and increased revenue.

Analyzed customer behavior data to derive actionable insights for marketing strategies.

Collaborated with cross-functional teams to deliver data-driven recommendations.

Developed predictive models using scikit-learn to optimize customer targeting and improve campaign effectiveness.

Conducted data preprocessing and built machine-learning models to predict customer churn.

Collaborated with data engineers to ensure data quality and availability.

Utilized Python and scikit-learn to develop predictive models and contributed to the deployment of a customer churn prediction system.

Designed and deployed a sentiment analysis bot with an 85% accuracy rate, enabling real-time customer feedback analysis.

Developed an ID reader to extract key information from scanned documents, automating data extraction processes.

Reduced manual data processing time by 30%, significantly contributing to operational efficiency.

Developed a sophisticated trading bot utilizing OHLC data and machine learning algorithms to identify trends and execute profitable trades.

Built object detection models with TensorFlow and PyTorch, enhancing image classification accuracy across various applications.

Collaborated cross-functionally to deliver ML solutions, ensuring seamless model integration and providing comprehensive documentation and post-deployment support.

Increased trading bot accuracy by 15%, directly driving higher profitability and enhancing user trust.