Omar Sayed Mostafa, Developer in Cairo, Cairo Governorate, Egypt
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Omar Sayed Mostafa

Verified Expert  in Engineering

AI Engineer and Developer

Location
Cairo, Cairo Governorate, Egypt
Toptal Member Since
August 7, 2023

Omar is a seasoned senior AI engineer with 6+ years of experience in the AI industry, specializing in computer vision, natural language processing, and audio processing. Omar's journey into the world of AI has been fascinating, as he has actively contributed to multiple real-life AI products from inception to deployment. With a combination of hands-on experience and advanced academic training, Omar is well-equipped to tackle any AI challenge.

Portfolio

Rosalyn
GStreamer, Apache Kafka, PyTorch, Python, C++, Audio, Linux, Git...
Silver Bullet Services Group
Docker, TensorFlow, Python, PyTorch, OpenCV, Linux, Git, Amazon S3 (AWS S3)...
Botit
Rasa NLU, Natural Language Understanding (NLU)...

Experience

Availability

Part-time

Preferred Environment

Linux, Git, PyTorch, Python, Docker, Natural Language Processing (NLP), Computer Vision, Runtime Optimization, Neural Networks

The most amazing...

...project I've led was at precision.ai. It involved diving into the architectural optimization of complex AI models, tailoring them for Jetson boards.

Work Experience

Senior AI Engineer

2022 - PRESENT
Rosalyn
  • Collaborated closely with a diverse, cross-functional, multinational team to translate intricate product requirements into advanced AI/ML models and architect innovative solutions.
  • Worked on developing retrieval-augmented generation (RAG) applications leveraging large language models (LLMs) through various frameworks such as Hugging Face, LangChain, and OpenAI APIs.
  • Was continuously involved in fine-tuning prompts (prompt-tuning) tailored for different large language models (LLMs) to adapt to the evolving downstream tasks of the product seamlessly.
  • Developed video analysis modules to detect and analyze different video activities in real-time streams, including object detection, object tracking, pose detection, gaze detection, segmentation, and motion detection.
  • Built models for voice activity detection and speech recognition (detecting and converting speech to text) within audio streams.
  • Maintained a multiprocessing multithreaded environment, enabling parallel execution of diverse AI models to significantly improve performance and reduce processing time.
  • Enhanced and optimized current working AI systems with end-to-end responsibility, including design, code implementation, training, testing, and deployment.
  • Implemented API endpoints to enable AI model inference, fostering real-time predictions and seamless integration.
  • Containerized models using Docker for streamlined deployment.
Technologies: GStreamer, Apache Kafka, PyTorch, Python, C++, Audio, Linux, Git, Amazon S3 (AWS S3), Docker, Parallel Programming, Computer Vision, Video Analysis, Audio Analysis, Amazon EC2, Natural Language Processing (NLP), Image Processing, You Only Look Once (YOLO), Object Detection, Semantic Segmentation, Python 3, Artificial Intelligence (AI), Machine Learning, Supervised Machine Learning, Image Recognition, Generative Pre-trained Transformers (GPT), Large Language Models (LLMs), APIs, ChatGPT, Speech Recognition, Speech to Text, Conda, TensorBoard, Neural Networks, Videos, LangChain, Image Search, Labeling, Data Preprocessing, Distributed Computing, Prompt Engineering, LoRa, Natural Language Toolkit (NLTK), PEFT, SpaCy, OpenAI, Generative Pre-trained Transformer 3 (GPT-3), Retrieval-augmented Generation (RAG), Data Engineering, Whisper

Senior Computer Vision Engineer

2022 - 2022
Silver Bullet Services Group
  • Collaborated remotely with a multinational team on a video-text retrieval task via Contrastive Language-image Pre-training learning and audio classification.
  • Worked on auto-greedy label generation for large datasets and used active learning to optimize automatic labels, resulting in faster annotation and improved model performance.
  • Fine-tuned large complex models, including a language encoder and an image encoder, using the customized dataset with generated labels, optimizing model performance for specific tasks.
Technologies: Docker, TensorFlow, Python, PyTorch, OpenCV, Linux, Git, Amazon S3 (AWS S3), Active Learning, Computer Vision, Image Processing, Video Analysis, Audio Analysis, Amazon EC2, Parallel Programming, Deep Learning, Object Detection, Python 3, Artificial Intelligence (AI), Machine Learning, Supervised Machine Learning, Image Recognition, Large Language Models (LLMs), Visualization, Conda, TensorBoard, Neural Networks, Videos, Image Search, Labeling, Data Preprocessing

Senior Machine Learning Engineer

2022 - 2022
Botit
  • Improved existing NER models for Arabic and English languages by studying the behavior of the models and spotting model weaknesses and lack of information representation in the training data.
  • Generated a balanced synthetic robust representation of entities and intents across different domains to represent ambiguity in the language and cover the corner cases the model failed to understand.
  • Updated a labeling schema for the training data to give the model more information during training enabling it to understand the context of the input text and differentiate between different domains of ambiguity.
  • Implemented training codebase using the Rasa platform for training transformer-based AI models.
Technologies: Rasa NLU, Natural Language Understanding (NLU), Natural Language Processing (NLP), PyTorch, Python, Linux, Git, Docker, Amazon S3 (AWS S3), Deep Learning, Python 3, Rasa.ai, Artificial Intelligence (AI), Machine Learning, Supervised Machine Learning, Large Language Models (LLMs), Conda, TensorBoard, Neural Networks, Labeling, Recurrent Neural Networks (RNNs), Data Preprocessing

Senior Machine Learning Engineer

2020 - 2022
Precision.ai
  • Researched and applied SOTA approaches to detecting and spraying weeds across different crops in different fields, leveraging supervised, self-supervised, and intermediate-supervised learning while working alongside a multinational team.
  • Implemented and trained autoencoder models for image semantic segmentation, in addition to using transformer-based models to apply knowledge distillation learning.
  • Worked on a model runtime optimization project that included model architecture tweaks and quantization-aware training to increase target model throughput and decrease latency while maintaining accuracy.
  • Led a small team responsible for optimizing a model–architecture-wise–to satisfy runtime requirements on high-end devices such as NVIDIA Jetson boards while maintaining accuracy.
  • Utilized ONNX, TensorRT, and TorchScripts to provide enhanced and efficient model engines.
  • Developed an end-to-end pipeline model, including training, evaluation, and deployment of end devices.
  • Implemented parallel training procedures using PyTorch (Distributed Data Parallel) to speed up the training process across multiple GPUs and multiple nodes, which saved more than 25% on Amazon EC2 instances training costs.
  • Provided semi-automated methods for semi-automated annotation of large crop datasets with minimal human interaction, leveraging active learning and other classic approaches to speed up the annotation process and save annotating costs from scratch.
Technologies: Open Neural Network Exchange (ONNX), NVIDIA TensorRT, Python, Active Learning, Deep Learning, Computer Vision, PyTorch, Amazon EC2, NVIDIA CUDA, Object Detection, Semantic Segmentation, Linux, Git, Amazon S3 (AWS S3), Docker, OpenCV, Image Processing, You Only Look Once (YOLO), Parallel Programming, Python 3, Artificial Intelligence (AI), Machine Learning, Supervised Machine Learning, Image Recognition, Conda, Neural Networks, Videos, Image Search, Labeling, Data Preprocessing, Distributed Computing

Computer Vision Engineer

2019 - 2020
Hansa Robotics
  • Contributed to the optimization and quantization of object detection models to fit the performance of edge devices.
  • Trained and deployed real-time object detection models (YOLO) on custom datasets using PyTorch.
  • Collected and annotated custom datasets from real product sources and generated further synthetic data to increase and balance the representation of datasets used in training.
  • Deployed object detection models (YOLO) on edge boards (Jetson boards) using TensorRT and DeepStream pipeline.
  • Converted PyTorch models using ONNX to TensorRT engine.
Technologies: Image Processing, You Only Look Once (YOLO), Object Detection, TensorFlow, Python, OpenCV, Linux, Git, Computer Vision, PyTorch, Deep Learning, Semantic Segmentation, Video Analysis, Python 3, Artificial Intelligence (AI), Machine Learning, Supervised Machine Learning, Image Recognition, Visualization, Conda, Neural Networks, Videos, Labeling, Data Preprocessing

PyTorch Segmentation for Upper and Lower Jaws

https://github.com/OmarSayedMostafa/Face-analysis
This project focuses on the implementation of semantic segmentation using PyTorch for the upper and lower jaws, specifically the maxilla and mandible, in a DICOM file. The dataset provided consists of 2D slices from all three orthogonal points of view: axial, coronal, and sagittal.

Deep Learning Colorization for Visual Media

https://github.com/OmarSayedMostafa/Deep-learning-Colorization-for-visual-media
This project focuses on automatic colorization for grayscale images and videos using deep learning techniques with Python and TensorFlow. It is a TensorFlow implementation of the research paper titled "Let there be Color!: Joint End-to-end Learning of Global and Local Image Priors for Automatic Image Colorization with Simultaneous Classification" by Iizuka et al.
2020 - 2023

Master's Degree in Computational Linguistics

Helwan University - Cairo, Egypt

2013 - 2017

Bachelor's Degree in Computer Science

Ain Shams University - Cairo, Egypt

Libraries/APIs

PyTorch, OpenCV, TensorFlow, Natural Language Toolkit (NLTK), SpaCy, Rasa NLU

Tools

Git, Open Neural Network Exchange (ONNX), You Only Look Once (YOLO), ChatGPT, TensorBoard, Whisper, Rasa.ai

Languages

Python, Python 3, C++

Paradigms

Parallel Programming, Distributed Computing

Frameworks

GStreamer

Platforms

Linux, Amazon EC2, Docker, NVIDIA CUDA, Apache Kafka

Storage

Amazon S3 (AWS S3)

Other

Natural Language Processing (NLP), Deep Learning, Computer Vision, Image Processing, Object Detection, Semantic Segmentation, Artificial Intelligence (AI), Machine Learning, Supervised Machine Learning, Large Language Models (LLMs), Conda, Neural Networks, Videos, Labeling, Data Preprocessing, Active Learning, Audio, NVIDIA TensorRT, Natural Language Understanding (NLU), Video Analysis, Audio Analysis, Image Recognition, Generative Pre-trained Transformers (GPT), Visualization, APIs, Speech Recognition, Speech to Text, LangChain, Image Search, Recurrent Neural Networks (RNNs), OpenAI, Generative Pre-trained Transformer 3 (GPT-3), Retrieval-augmented Generation (RAG), Data Engineering, Scientific Computing, Runtime Optimization, Prompt Engineering, LoRa, PEFT, ChatGPT Prompts

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