Daniel Adama

Serving as a Forward Deployed Engineer, architecting and deploying bespoke AI systems for clients. Built a real-time multi-tenant bank reconciliation system matching 220K+ transactions in under 12 minutes.

Handled server-side and infrastructure issues, resolved critical bugs, and improved backend stability. Conducted research to reduce LLM inference costs and integrated models with extended context windows.

Architected and deployed a FastAPI-based Agentic AI Workflow for mental-health support. Integrated MongoDB Atlas vector search with SentenceTransformers embeddings for sub-second knowledge retrieval.

Extended George's capabilities for real-time audit guidance and refined LLM outputs. Designed and implemented an Account AI Assistant with tool access for report generation, trial balance calculations, and journal uploads.

Built a long-term memory system using Qdrant and OpenAI, and developed a chat stylizer for personalized responses. Designed a time-sensitive semantic search feature for Twitter.

Spearheaded development of George, the world's first AI auditor, and engineered end-to-end AI audit automation. Ensured compliance with global audit standards such as ISA, IAASB, and GAAS.

Led a project to annotate images and developed a YOLOv8-based object detection system trained on 142,000 images. Achieved 96% accuracy in color detection and integrated image-to-text capabilities.

Gathered and processed data for training an open-source Generative AI, and wrapped the LLM in a FastAPI framework. Deployed the system on AWS for scalable performance.

Developed an end-to-end exercise tracking system using Python and FastAPI, and implemented user authentication. Trained a TensorFlow model for exercise classification and reduced model size by 40% for web deployment.

Collaborated with a team to design and develop software solutions using the .NET stack, including C#, ASP.NET, and SQL Server. Worked closely with senior developers to learn best practices.

Built and improved prediction models using Sklearn, Pandas, TensorFlow, and Gradient Boosting. Assessed data quality and used data-driven insights to reduce transportation costs.

Completed a real-time Face Recognition system using Dlib, OpenCV, and Imutils. Researched and implemented a Speech Recognition pipeline with Deepspeech and Webrtcvad for a video conferencing platform.

Downloaded image data from Google and Kaggle for training, and trained 2710 images achieving 98% accuracy. Built a real-time Covid-19 Face Mask Detection system.

Deployed machine learning algorithms like Naive Bayes, SVM, and Random Forests on Heroku. Scraped data from websites with Python Scrapy and created a web application for predictions.

Completed the Complete guide to ASP.NET Core MVC (.NET 6) course on Udemy in January 2023.

Completed the Getting started with Docker course on Simplilearn in January 2022.

Completed the Machine Learning Certification Course on Analytics Vidhya in January 2022.

Grade: Second Class Upper Division

Bachelor of Engineering in Information and Communication Engineering from Covenant University, Ota, Ogun State, Nigeria, from August 2015 to July 2021. Graduated with Second Class Upper Division.

Completed the Fundamentals of Deep Learning Object Detection - Object Detection 201 course on PyImageSearch in May 2021.

Completed the Bounding Box Regression - Object Detection 202 course on PyImageSearch in May 2021.

Completed the OpenCV Basics - OpenCV101 course on PyImageSearch in May 2021.

Completed the Data Science Course 2020 on 365 Data Science in December 2020.

Completed the Neural Networks and Deep Learning course on DeepLearning.AI in August 2020.