Transforming complex data into intelligent AI solutions. Specializing in LLMs, Machine Learning, and cutting-edge Prompt Engineering techniques.
Passionate about AI and data-driven solutions
Senior AI Innovation Engineer | Ex-Prompt AI Engineer at Morgan Stanley | Ex-ML Associate at Vector Institute | Data Scientist | Gen AI | LLMs
I'm a highly motivated and detail-oriented Data Scientist with expertise in statistical modelling, machine learning, Bayesian Statistics, Large Language Models (LLMs), and Prompt Engineering. Skilled in transforming complex datasets into actionable insights.
With extensive experience in cloud computing (AWS, GCP, Oracle) and advanced AI tools (OpenAI, LangChain, Gemini, GroqAI), I apply cutting-edge technologies—including LLMs, Prompt Engineering, Retrieval-Augmented Generation (RAG), and Vector Databases.
I'm currently advancing my knowledge in Generative AI, Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). Looking to collaborate on open-source projects and innovative AI solutions.
Building AI solutions across industries
Building agentic AI systems, Knowledge Graph, and application modernization solutions using Software Archaeology and BMAD methodologies. Applying advanced prompt engineering, context engineering to deliver scalable, intelligent products.
Leading prompt engineering initiatives for the company's HR Bot application to answer employee questions through sophisticated and state-of-the-art prompt design and optimization techniques.
Advanced leadership development through Generative AI and prompt engineering techniques. Developed dynamic meeting summarization modules and integrated knowledge graphs with RAG techniques.
Built and deployed multiple RAG-based chatbots using LangChain, Pinecone, Voyage AI, and AWS. Developed an AI voice agent with real-time speech-to-response pipelines. Designed and optimized LLM prompts, safety layers, and conversational flows for production systems.
Developed ResSum, a custom LLM-based research synthesis package. Built multi-agent RAG systems for systematic reviews. Applied Bayesian modeling (INLA-SPDE) and ensemble ML for species distribution modeling. Published at NeurIPS (2024 & 2025).
Analyzed 30 years of methane emissions time series data in Canada using advanced machine learning and statistical forecasting techniques (ARIMA, SARIMA, PROPHET).
Tools and technologies I work with
Showcasing AI solutions and innovations
A Python package leveraging LLMs for research synthesis and high-level text summarization, using open-source LLM models (Llama) to streamline systematic literature reviews.
An AI-powered chatbot using LLMs that provides users with detailed scholarship information, eligibility criteria, and application guidance.
AI-driven chatbot using RAG techniques to provide diabetes information. Implemented with LangChain, Pinecone for vector storage, and AWS services.
A comprehensive Python package for Google Drive interactions, enabling seamless file operations such as download, upload, and PDF to text conversion.
Machine learning models to forecast greenhouse gas emissions in Canada using 30 years of historical data with SARIMA and Prophet models.
Advanced ML ensemble system predicting monthly fire risk across Montreal neighborhoods using Random Forest, XGBoost, and LSTM models. Features spatial-temporal analysis with 71% accuracy and adaptive risk stratification for emergency response planning.
Ready to collaborate on your next AI project
I'm always interested in new opportunities and exciting projects. Whether you need AI consultation, prompt engineering, or data science expertise, let's discuss how we can collaborate.
akandehammedadedamola@gmail.com
Canada (Montreal | Ottawa)
Open to Remote Work & Relocation