Responsibilities & achievements

- Spearheaded the design and implementation of a robust ML ecosystem from the ground up, seamlessly integrating Large Language Models (LLMs) to serve user requests with high-quality responses. - Leveraged cutting-edge prompt engineering techniques, fine-tuning LLM outputs to provide tailored assistance for parents on their parenting journey. Implemented state-of-the-art LLM models and advanced techniques like Retrieval-- Augmented Generation (RAG) using a proprietary knowledge base to enhance response accuracy and relevance.

Responsibilities & achievements

- Drove the exploration of various neural network architectures for object and key-point detection using Python, TensorFlow, Keras, and the TF-Object Detection API. - Demonstrated advanced skills in data preprocessing and labeling. - Achieved a 40% increase in detection accuracy and a 30% reduction in false positives, significantly improving the application's performance and reliability. - Showcased exceptional proficiency in data preprocessing and labeling techniques, resulting in a 25% reduction in training time for neural networks and a 20% increase in overall model efficiency. - Pioneered the successful integration of an advanced OpenCV algorithm to elevate hand image processing capabilities, culminating in a 35% increase in image recognition accuracy and a 25% improvement in real-time processing speed, amplifying the app's functionality and user experience. - Orchestrated the successful deployment of sophisticated deep learning models on both iOS and Android platforms for seamless on-device inference. - Utilized Python to craft APIs, optimizing model deployment via Google Cloud micro-services and ensuring robust performance. - Marked a dynamic blend of research, coding, and cross-platform development, leading to impactful outcomes and enriched user experiences. - Responsible for spearheading the exploration, development, and seamless deployment of sophisticated deep learning and computer vision algorithms. - Leveraged expertise in deep learning techniques, and successfully enhanced object detection, recognition, and tracking functionalities, resulting in immersive and engaging interactions for users. - Worked on end-to-end creation of mobile applications for both Android and iOS platforms. - Demonstrated a profound understanding of deep learning, computer vision, Python programming, and mobile app development.

Responsibilities & achievements

- Conducted comprehensive research into state-of-the-art NLP techniques, employing them to create impactful vector embeddings and deploy effective ML algorithms for multiclass document classification. - Achieved a remarkable 45% improvement in classification accuracy and a 30% reduction in processing time, optimizing document categorization for enhanced business operations. - Formulated and presented a compelling proof of concept proposal, showcasing a novel approach to custom document-type classification, contributing to client satisfaction.

Responsibilities & achievements

- Led a pivotal project for Legalario, involving the creation of an object detection API to accurately identify personal IDs from images. - Delivered an outstanding 90% accuracy rate in ID detection, significantly streamlining document verification processes. - Conducted meticulous manual image labeling for personal IDs and adeptly trained an SSD neural network employing Python and the TF-Object Detection API. Realized a remarkable 40% reduction in false positives and a 30% improvement in detection speed, showcasing exceptional proficiency in model training. - Architected and implemented a robust API using Python and Flask, successfully achieving seamless identification, classification, and processing of valid IDs through sophisticated OpenCV techniques. This accomplishment led to a 50% increase in processing efficiency, expediting ID validation procedures and enhancing overall operational effectiveness. - Achieved seamless integration of the API into Legalario's systems, contributing to improved accuracy and efficiency in personal ID verification processes.

Responsibilities & achievements

- Spearheaded an innovative project aimed at predicting real age from single images, even without facial landmarks, showcasing a profound understanding of deep learning principles. - Employed Python and TensorFlow to develop a specialized variant of the Deep EXpectation (DEX) method tailored for mobile devices, demonstrating adaptability and efficiency. - Masterfully curated and augmented a diverse dataset of facial images, meticulously preparing it for training a lightweight yet highly accurate deep neural network. - Orchestrated the successful deployment of the model in an Android app, enabling users to capture and edit selfies for instant age prediction, showcasing advanced mobile app development skills.

Responsibilities & achievements

- Embarked on an extensive endeavor to classify diverse dog breeds, resulting in the successful creation of a cross-platform Android and iOS application for precise dog breed identification. Garnered over 100,000 combined downloads within the first six months of launch, attesting to the app's popularity and accurate classification capabilities. - Curated an extensive dataset comprising images of 139 distinct dog breeds, meticulously preparing it for training a deep neural network. - Exhibited exceptional data preprocessing and augmentation techniques, resulting in a model achieving an impressive accuracy rate surpassing 83%. - Crafted a user-friendly mobile application that enables users to effortlessly identify dog breeds by capturing new images or utilizing photos from their gallery.

Responsibilities & achievements

- Undertook a sophisticated data analysis endeavor, processing a labeled dataset of facial images to redefine beauty scores and classify them into distinct attractiveness classes. - Leveraged Keras and harnessed the power of the DenseNet121 architecture to construct a cutting-edge CNN model for estimating facial attractiveness. - Innovatively combined disparate datasets, encompassing human faces and random images, to train a model with the ability to discern facial images. - Demonstrated prowess in model optimization, converting models into efficient TFLite models and further refining their sizes using post-training float16 quantization. - Successfully deployed the models on an Android app, equipped with GPU delegates, empowering users to gauge the attractiveness of facial images with remarkable accuracy.