Passionate AI Engineer with 8+ years of experience in Machine Learning (ML) model design, fine-tuning, testing and deployment. Expertise with Computer Vision, Generative AI (GenAI), Natural Language Processing (NLP) with…
Passionate AI Engineer with 8+ years of experience in Machine Learning (ML) model design, fine-tuning, testing and deployment. Expertise with Computer Vision, Generative AI (GenAI), Natural Language Processing (NLP) with Large Language Models (LLM), Data Analysis, and most importantly delivering AI/ML solutions across diverse industries with GCP AI services. Highly experienced in programming with Python (TensorFlow/Keras), Matlab as well as Web development Languages including Frontend and Backend Languages. Possess passion for AI with comprehensive knowledge of machine learning concepts and other related technologies. Excellent communication and presentation skills in describing technical issues with co-workers and managers.
• Participated in developing Large Language Model based on GPT, BERT and FLAN-T5 with parameter efficient fine-tuning (PEFT) to ensure task specific requirements.
• Led the successful implementation of LoRA fine-tuning for a healthcare question answering task, achieving superior performance in clinical information retrieval.
• Developed Machine Learning (ML) model particularly Long-Short Term Memory (LSTM) with Python and Matlab to predict financial time series, outperformed 1.2% than up-to-date forecasting models with low latency.
• Tutored junior engineers to build Deep Neural Network (DNN) models and encouraged them to do without any library and framework.
• Participated in developing web applications by integrating machine learning architectures into both front-end and back-end systems, enriching user experiences and enabling data-driven functionalities at all levels of the application stack.
Participated in developing Large Language Model based on GPT, BERT and FLAN-T5 with parameter efficient fine-tuning (PEFT) to ensure task specific requirements.
Led the successful implementation of LoRA fine-tuning for a healthcare question answering task, achieving superior performance in clinical information retrieval.
Developed Machine Learning (ML) model particularly Long-Short Term Memory (LSTM) with Python and Matlab to predict financial time series, outperformed 1.2% than up-to-date forecasting models with low latency.
Tutored junior engineers to build Deep Neural Network (DNN) models and encouraged them to do without any library and framework.
Participated in developing web applications by integrating machine learning architectures into both front-end and back-end systems, enriching user experiences and enabling data-driven functionalities at all levels of the application stack.
Developed Convolutional Neural Network (CNN) models for specific classification tasks, with a successful deployment on Web and Android platforms, ensuring seamless integration and usability across multiple devices.
Led a multidisciplinary team to develop a state-of-the-art convolutional neural network (CNN) model in healthcare innovation, achieving an impressive 97% accuracy in classifying X-ray images, revolutionizing medical imaging diagnostics and setting a new standard for precision in healthcare analytics using PyTorch framework.
Implemented Convolutional Neural Network (CNN) architectures within a web application designed for tutoring, leveraging advanced deep learning techniques to enhance the learning experience through personalized and interactive content delivery.
As a team member, I played a pivotal role in the development of an image classification task tailored for educational purposes in the biology field, specifically focusing on categorizing various species of plants and flowers, thus contributing to the creation of a valuable resource for botanical education and fostering a deeper understanding of biodiversity and ecological principles through innovative technology.
Designed and trained state-of-the-art Machine Learning (ML) models for Natural Language Processing (NLP) tasks, such as sentiment analysis, named entity recognition, and machine translation, achieving high accuracy and performance through meticulous model architecture design and training.
Participated in optimizing model architectures and hyperparameters, also effective algorithm to achieve superior performance metrics in machine learning tasks with Python and PyTorch as well as Tensorflow.
Experienced in leveraging Machine Learning (ML) methodologies to construct phonetic representations, involving tasks such as feature extraction, modeling, and analysis to effectively capture and represent speech patterns and phonetic nuances.
As an intern, learned how to bridge the gap between academic knowledge and real-world application.
Engaged in learning diverse front-end and back-end web technologies, with a notable emphasis on SQL databases, expanding my skills to contribute effectively to the development of comprehensive web applications capable of managing complex data structures with precision and efficiency.
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