Daniel Enemona Adama Email & Phone Number

Federal Capital Territory, Nigeria

• Developed and maintained an end-to-end exercise tracking system using Python and FastAPI backend, ensuring high performance and scalability.• Implemented robust and secure user authentication mechanisms, enhancing user data protection and system security.• Collected image data from videos and trained a TensorFlow model to accurately classify exercises, improving the system's recognition capabilities.• Converted TensorFlow models to TensorFlow Lite for deployment on mobile devices, web browsers, optimizing models for efficient client-side execution.• Utilized MoveNet for real-time exercise tracking and feedback, leveraging advanced computer vision techniques to improve user experience and accuracy.• Managed cloud infrastructure on Digital Ocean (Kubernetes, Object Store, Container Registry), optimizing resource allocation and ensuring system reliability.• Executed complex database operations with PostgreSQL, maintaining data integrity and enhancing performance through efficient query optimization.• Collaborated closely with cross-functional teams, including frontend developer, and product manager, to continuously enhance system features and improve overall user experience.

• Spearheaded a project to annotate images using Roboflow and CVAT.• Developed and fine-tuned a YOLOv8-based object detection system, which was trained on an extensive dataset of over 144,000 images. This rigorous training process resulted in a highly accurate and reliable model capable of detecting various objects in real-time scenarios.• Achieved an exceptional 96% accuracy rate in color detection by fine-tuning a VGG-16 model, enabling precise identification of colors within the visual data.• Integrated image-to-text capabilities into the system, enabling real-time summarization of video frames to provide enhanced context understanding and improve user engagement.• Led a diverse team of Frontend Engineers and UI/UX Designer.• Leveraged Apache Kafka for real-time data streaming and message queuing, optimizing communication between system components and facilitating scalable deployment across diverse hardware platforms.• Implemented serverless GPU processing using Runpod, maximizing computational efficiency and resource utilization for intensive computer vision tasks and also saving cost.• Integration of computer vision with real-world applications.• Full project management from data collection to deployment.• Documented comprehensive project processes and technical specifications, ensuring clarity, continuity, and knowledge transfer among team members.• Conducted extensive testing and validation of object detection models across multiple YOLOv8 variants (Small, Nano, Medium), ultimately selecting YOLOv8 M for final model training due to its superior performance and accuracy in object detection tasks.• Orchestrated the setup of CCTV cameras to capture video data for model training and testing, ensuring optimal placement and configuration to cover key areas of interest.

• AI Consultant specializing in computer vision, natural language processing, and predictive modeling.• Proven track record of developing and deploying machine learning models for practical applications.• Actively involved in building an artificial intelligent exercise tracking system for a Health organization.• Technologies: OpenCV, Python, Mediapipe, Numpy, Pandas.• Implemented a Face Recognition System on images with successful deployment in Docker on EC2.• Utilized S3 bucket for efficient storage in the deployment process.*

Lagos, Nigeria

Abuja, Federal Capital Territory, Nigeria

• Design and Automated 76 processes for the Office of the Head of Civil Services of the Federation.• Automated business processes for the Federal Ministry of Finance • Lead a team of 4 in the successful completion of a project

Lagos, Nigeria

• Responsible for building and improving the prediction models with Sklearn, Pandas, TensorFlow, Gradient Boosting Algorithms, NLTK, OpenCV etc.• Assessed data quality and cleansed data for further processing.• Used data-driven insights to reduce transportation costs.

Bangalore Urban, Karnataka, India

• Completed a real-time Face Recognition system that recognizes and tags known faces accordingly with Dlib, OpenCV, Imutils, etc.• Researched and Implemented a Speech Recognition pipeline with Deepspeech, Webrtcvad, Pyaudio, etc. for a real-time video conferencing platform.• Developed a Face and Eye-tracking algorithm including Face mask detection for real-time purposes with OpenCV, Tensorflow, etc.• Utilized Deep Learning Frameworks like Tensorflow, Keras, OpenCV, Dlib, and Face recognition for Computer Vision use cases.• Reports feedback to the team on the progress of the project• Worked with 3 different pre-trained models which are ResNet50, EfficientNetB1, and MobileNet V2.• Created various charts in Jupyter Notebook using Matplotlib to perform a preliminary analysis on the collected data.

Singapore, Singapore

- Downloaded Image data from Google and Kaggle for training purposes.- Trained 2710 images on my local machine and got a 98% accuracy.- Built a real-time Covid-19 Face Mask Detection system for the detection of a person with a mask or not.

New Delhi, Delhi, India

- Deployed Machine Learning algorithms like Naive Bayes, SVM, Random Forests, Logistic Regression, etc on Heroku.- Scrapped data from diverse websites.- Transformed raw data to conform to assumptions of machine learning algorithm.- Investigated available resources to develop more useful project plans.- Created a web application to make critical predictions.- Carried out day-to-day duties accurately and efficiently.

Abuja, Federal Capital Territory, Nigeria

- Cooperated with a team of 6 to develop a drone for the fertilization of farm crops through the use of Satellites.- Volunteered with a team of 8 to the building of a Radio Frequency (RF) antenna to communicate via abandoned Satellites.- Participated in an ongoing project that detects any airplane from a distance that crosses over NASDRA