Junior Machine Learning Engineer, Nodes Digital Limited [2023-Present]
- Developed and maintained a scalable Machine Learning CodeBase Framework for backend systems, integrating various machine learning models into API workflows to ensure streamlined data flow and efficient model deployment.
- Implemented FastAPI for robust API integration, creating a fast, secure, and efficient communication pipeline between machine learning models and system components, enhancing deployment speed by 25%.
- Dockerized the entire machine learning environment, enabling portable and consistent development setups across multiple systems, reducing deployment times by 30%.
- Designed and organized the project’s file structures using Object-Oriented Programming (OOP) principles, enhancing code clarity, reusability, and maintenance.
- Conducted hyperparameter tuning using tools such as GridSearchCV and Keras-Tuner, optimizing model performance for real-world datasets and improving accuracy by 15%.
- Managed the full lifecycle of ML models, from training to deployment, ensuring the models are production-ready, including monitoring model performance and adjusting models based on feedback and new data.
- Collaborated cross-functionally with software, IoT, and DevOps teams to ensure seamless integration of machine learning models into broader system architectures.
- Managed version control through Git and GitHub, ensuring smooth collaboration and project documentation, facilitating future scalability and enhancement.
- Developed predictive models for the E-Irrigation and E-Fisheries projects, employing computer vision techniques such as image annotation, preprocessing, and custom model development using attention mechanisms.
- Worked on NSCLC Subtype Classification, extracting radiomics features from lung images to classify tumors using machine learning algorithms.
- Collaborated with international partners, securing funding from the Institute of Advanced Research (IAR).
- Applied machine learning models to medical imaging data, contributing to a research paper currently under review.
- Led lab sessions and classroom discussions, guiding students through complex topics such as data structures and algorithms.
- Conducted pre-lab and post-lab sessions to ensure students were prepared for practical exercises.
- Delivered lectures and managed labs in the absence of professors, ensuring continuity in learning.
- Provided academic counseling, helping students with course-related queries and guiding their progress.
- Evaluated student assignments, quizzes, and exams, ensuring consistent grading according to rubrics.
- Provided detailed feedback on assignments, helping students improve their academic performance.
- Maintained accurate grade records and assisted in developing exam questions to align with course objectives.
- Conducted three full sessions of C Programming classes titled “Programming for Beginners”, aimed at newly admitted junior students.
- Taught over 150+ students through an online platform, covering fundamental concepts in C programming, ensuring a solid foundation for future coursework.
- Organized and managed assignments to reinforce learning and applied practical coding exercises to track student progress.
- Hosted a Project Show under a seminar, where over 100+ students presented their projects, showcasing what they had learned in the course.
- Provided individual feedback and mentorship throughout the course, ensuring students gained both theoretical and practical skills in programming.
