Uloko Emmanuel

Designed and implemented a Legal LLM Conversational Application leveraging a RAG pipeline, incorporating node generation, contextual metadata enrichment, embedding generation, and PII redaction on legal documents. Integrated pgvector as vector store to optimize semantic retrieval for legal practitioners and students. Built and deployed a complete ML pipeline on Azure, integrating MLflow for experi

Trained client’s LLM using Reinforcement Learning for Human Feedback (RLHF) and Supervised Fine-tuning (SFT) techniques to significantly improve its performance. Focused on enabling the client's LLM to build software applications from scratch.

Developed a spoofed image detector system to curb fraudulent acts in the onboarding process, significantly enhancing security measures. Engineered a recommendation system integrated into the bank application to boost user engagement and developed a RAG chatbot powered by an LLM to provide product/service insights and facilitate transactions.

Analyzed credit data, optimizing loan approvals and portfolio management. Developed predictive models for customer churn and credit risk assessment, and implemented data and machine learning pipelines to streamline processes.

Contributed to building a minimum viable product, gaining hands-on experience in data science. Led a team of interns on a UN SDG Twitter analytics project, effectively presenting findings, and completed comprehensive training in data analysis, machine learning, and model deployment.

Collaborated on market analysis and investment strategies, conducting data visualization to present insights to stakeholders. Performed statistical analysis on new marketing strategies to inform business decisions.

Grade: First Class (Summa Cum Laude)

Achieved a First-Class degree in Mechanical Engineering, demonstrating strong foundational knowledge in engineering principles. Completed coursework covering core mechanical engineering subjects.

Activities and societies: Led a group of interns in the development of predictive systems including Spain's electricity load shortfall, climate change sentiment analysis, and a movie recommendation system.

Completed a comprehensive Data Science/ML Training Programme, covering SQL, Python, data cleaning, visualization, statistical analysis, machine learning, model deployment, and deep learning. Led a group of interns in developing predictive systems for electricity load shortfall, climate change sentiment analysis, and movie recommendation.