Hi, I'm Leon Nwankwo [Chike],

As the Systems Architect and Product Strategist, I spearheaded the development of an advanced error resolution application that utilizes pre-trained OpenAI model to analyze production errors, provide detailed error descriptions, and suggest targeted resolutions. By integrating AI into the error resolution workflow, we significantly reduced the time required for software engineers to identify and resolve issues, resulting in improved application stability.

To ensure that our system had access to the most recent codebase, I architected and implemented a server that integrated with our GitHub repository through webhooks. This server automatically fetched the latest pull requests and branches, enabling the custom AI model to access our codebase in real-time. By leveraging this integration, the application could perform in-depth code analysis and provide highly relevant and context-specific error resolutions.

On the presentation layer, I collaborated closely with our UX/UI team to design an intuitive interface that presents error information and recommended resolutions in a clear and actionable manner.

Throughout the development lifecycle, I conducted thorough testing and iterative refinements to enhance the accuracy of the AI models in generating error descriptions and resolutions. The resulting tool empowers engineers to efficiently resolve production errors, reducing mean time to resolution (MTTR) and saving valuable development resources.

I designed and programmed a new onboarding experience to drive comprehension and improve engagement rates for new users, delivering on a company-level initiative of aligning orders with items lost in natural disasters.

Our custom-trained model, built using OpenAI's platform, employs a combination of natural language processing (NLP) and computer vision algorithms to extract relevant information from the uploaded documents. The NLP component analyzes the text within the documents, identifying key details such as item descriptions, quantities, and prices. Meanwhile, the computer vision algorithms process the uploaded images, recognizing and categorizing the items present.

Once the information is extracted, the LLMs come into play, generating accurate and context-aware product descriptions and user profiles. By understanding the context and semantics of the extracted information, the LLMs can create highly personalized and relevant content for each user.

The result is a streamlined onboarding process that automatically pre-generates users' carts and profiles based on their uploaded documents. This intelligent automation saves an average of 35 minutes per user, significantly enhancing the user experience and reducing the time and effort required to place orders for items lost in natural disasters.

I spearheaded the development of an innovative Robotic Process Automation (RPA) application that transformed pension processing for a major client. By leveraging Azure Machine Learning (Azure ML) for supervised regression models and Azure Document Intelligence for unsupervised document clustering, we automated complex tasks, extracting data from 200 million scanned documents. The application freed 95,000 hours of manual labor, improved accuracy and efficiency, and benefited over 265,000 pension plan members.

The true challenge we faced was not the implementation of the technology itself, but rather building systems that triggered alerts and warnings when an individual's benefit could potentially be impacted by erroneous or missing data. This is where my team and I invested a considerable amount of time. We trained ML models using both supervised learning (regressions) in Azure ML and unsupervised learning (document clustering) with Azure Document Intelligence to build the algorithms that drove the project. We collaborated with actuaries, data scientists, and subject matter experts to identify financial and data risks. We then incorporated these risks into our algorithms to monitor, detect, and alert pension management teams to potential issues. Though no system is perfect, the iterative approach we adopted will live on with this project and continue to improve pension management for years to come.

This project showcased my ability to drive innovation, lead cross-functional teams, and deliver significant cost savings and operational efficiencies through cutting-edge technology solutions, leveraging advanced machine learning techniques and cloud-based platforms like Azure ML and Azure Document Intelligence.

Architected a scalable microservices platform using Node.js, Docker, and AWS ECS, enabling 4x customer growth and handling a 17x increase in API usage. The modular design facilitated rapid feature releases and the integration of a machine-learning recommendation engine, increasing average order value by 23%. Collaborated with cross-functional teams and drove process improvements, resulting in a 30% decrease in time-to-market and strengthening competitive positioning.