Machine Learning Developer
Wassim is a software engineer with 7+ years of experience, including 4+ years in machine learning. He worked with various clients, from startups to research institutes to multinational corporations. Wassim stands out from the crowd because he is thorough about building scalable solutions that are adaptive to business requirements.
ExperienceMachine Learning - 4 yearsArtificial Intelligence (AI) - 4 yearsComputer Vision - 4 yearsDeep Learning - 4 yearsOptimization - 4 yearsGPT - 3 yearsGenerative Pre-trained Transformers (GPT) - 3 yearsNatural Language Processing (NLP) - 3 years
PyTorch, PySpark, NumPy, Jupyter Notebook, Pandas, Amazon Web Services (AWS), Natural Language Processing (NLP), GPT, Generative Pre-trained Transformers (GPT), Computer Vision, Python, Artificial Intelligence (AI)
The most amazing...
...project I’ve built is a legal case classification model, a system that classifies case descriptions and highlights crucial text elements.
Machine Learning Engineer
- Cultivated a concept extraction from text, audio, and video content to understand social media trends better. Concepts include emotions, activities, and sentiments on what's going on in the video.
- Developed a set of Apache Airflow Directed Acyclic Graphs (DAGs) to orchestrate data and machine learning (ML) pipelines.
- Created a monitoring system to have a bird's-eye view of the whole system.
Lead Machine Learning Engineer
- Worked as a startup consultant for a real-estate startup to help them better understand their data infrastructure and guide them to the right tools using ETL pipelines, data lakes, delta tables, and hot storage.
- Developed a complete set of PySpark ETLs for transforming, cleaning, and normalizing data from different data sources and industries, including real estate and agriculture.
- Built a personalized ML-powered employee reward model as an MVP for an early-stage startup using customer-level data and rewards data from different providers.
- Managed the tech team, including designing the architecture for the whole team. The architecture ranges from web scrapers with dynamic proxies until the data is in hot storage, ready to be used by REST APIs.
- Developed machine learning models for consumer demand forecasters in the retail domain focusing on optimizing distribution to avoid out-of-stock.
- Worked on an algorithm for forecasting consumer demand for a certain set of products to help our client engage strategically in new markets.
- Implemented a solution using AWS Textract to extract structured data from PDF files.
- Developed an end-to-end pipeline for extracting and saving the data into a data lake.
- Provided alternative solutions and tools to improve the efficiency and accuracy of the data extraction process.
Senior AI | Tech | HR Consultant
Block Born LLC
- Advised on AI tools to generate creative suggestions for game content production based on schema and scale.
- Reviewed and provided feedback on ideas related to implementing an AI tool for creative content suggestions.
- Demonstrated deep expertise in AI and its applications for creative content generation.
Machine Learning Engineer
Advest Marketing, LLC
- Worked on optimizing a product ads algorithm for social media.
- Researched techniques for improving product placement in videos.
- Explored deep learning techniques for generative modeling for videos.
ML Engineer with Skills in GPT-2/3
- Trained a GPT-style model for simple language modeling to be deployed on a bit tensor network.
- Configured an iterative procedure to train models from previous checkpoints on new datasets.
- Monitored the model performance during and after training to know when to retrain.
- Worked on my Ph.D. thesis on the acceleration of a neural network using computer arithmetic.
- Devised new neural network training strategies to encourage better performances from low-precision neural networks.
- Developed custom CUDA operations for low-level operations and function approximations.
- Attended conferences and research schools related to machine learning and computer arithmetic.
- Worked on a research project to enable running big machine learning models on small resource-constrained devices using early exit networks.
- Collaborated with two professors to do a literature review on model compression techniques, including quantization, pruning, and knowledge distillation.
- Published a paper for an international conference and presented the work at the meeting.
Machine Learning Engineer
- Made an object detection model faster and lighter to be able to run in pseudo real time to be deployed on a self-driving car.
- Worked closely with the core machine learning team in order to make sure we were aligned on the experiment setup and results.
- Experimented with multiple model compression strategies like pruning, quantization, and compiling to evaluate the efficacy of each method.
- Compressed the model around 4x with 3x improvement in inference speed while maintaining the same performance as the original model.
- Developed a system that classifies legal cases from raw text input by an industry expert. The data given was raw scraped and OCR'd pdf from online sources.
- Performed data analysis of unstructured data to understand what could be done with the data and the needed processes to improve the quality.
- Built a web scraper to scrape financial news.
- Provided a sentiment analysis feature for financial news articles using transformer-based models.
Machine Learning Engineer
- Developed a customer-facing chatbot using RASA AI, Dialogflow, and Microsoft bot framework for a leading telecom operator for handling FAQ and account-related questions with OTP authentication and third-party integrations.
- Tracked issues in real-time using various tools like Sentry, ELK stack, and Docker monitoring tools.
- Handled meeting with the client and gathered various team requirements for the best launch process.
Head of the iOS Department
- Led and oversaw a team of iOS developers for two years.
- Managed the hiring and onboarding process for new hires.
- Migrated the team tech stack and incorporated a new software architecture.
- Developed commercial applications for clients ranging from one-person startups to multinational companies.
- Tracked issues happening on the application side in real time and resolved them in the next release.
- Migrated old applications from Objective-C to Swift.
Concept Extraction from Videos
The concepts include:
Quantized Neural Network for Object Detection
The model used is a MobileNetV2 pre-trained model on ImageNet with an SSDLite object detector. We trained the model in an FP32 data format.
I applied several model compression techniques to reduce the model's size and monitor its performance.
Some of the methods we used are:
• Fused convolution
• Knowledge distillation
The work concluded that the model could reliably detect objects in images with the same accuracy as the FP32 version while going as low as the INT8 data format.
Casual Language Model Fine-tuning
Legal Case Classification
• EDA on unstructured messy text data to understand what can be done with the data
• Build a model that would classify a legal case based on the description entered into several categories. This part aimed to provide a tool to help lawyers classify cases faster and more easily.
• Extract the entities from the case description relevant to the classification to help counterarguments.
Custom Language Model Training Frameworkhttps://github.com/pegesund/nor_bert
Multi-modal Text Classifier
Twitter Sentiment Analysishttps://twitter-sentiment.portfolio.wassimseifeddine.com/
Running Neural Networks on Edge Deviceshttps://ieeexplore.ieee.org/abstract/document/9664700
Norwegian Sentiment Analysis Model
The main difficulty was finding a good Norwegian labeled sentiment analysis dataset and fine-tuning an existing multilingual model.
Python, Python 3, Bash, SQL, Snowflake, C++, Swift
PyTorch, NumPy, Pandas, Matplotlib, XGBoost, PySpark, OpenCV, REST APIs, Scikit-learn, TensorFlow, YouTube API
Data Science, Mobile Development, ETL, App Development, Real-time Systems
Jupyter Notebook, Docker, Databricks, Amazon Web Services (AWS), Embedded Linux, iOS, AWS Lambda, NVIDIA CUDA, Blockchain, Microsoft Edge
NoSQL, MySQL, Data Lakes, Amazon S3 (AWS S3)
Machine Learning, Computer Vision, Sentiment Analysis, Deep Learning, Natural Language Processing (NLP), Artificial Intelligence (AI), Image Processing, OCR, Computer Vision Algorithms, Mobile App Development, Predictive Modeling, Algorithms, APIs, Data Analytics, Data Visualization, Forecasting, AI Design, Data Engineering, Deep Neural Networks, Data Analysis, CTO, Predictive Analytics, Statistics, Datasets, Causal Inference, Fine-tuning, Data Inference, Programming, Architecture, Analytics, Image Recognition, API Integration, GPT, Generative Pre-trained Transformers (GPT), Optimization, Consulting, Advisory, Technology Consulting, Startup Consulting, Facial Recognition, OpenAI, GPT-Neo, Google Publisher Tag (GPT), DeepSpeed, Text Generation, Speech Recognition, Web Development, Handwriting Recognition, Large Language Model (LLM), Data Management, Stable Diffusion, ControlNet, Graph Theory, Arithmetic, Object Detection, Neural Networks, Slurm Workload Manager, Chatbots, Recommendation Systems, Quantization, Data Scraping, Clustering, Text Classification, Internet of Things (IoT), Language Models, Data Warehousing, Trend Forecasting, Topic Modeling, Revenue Projections, Video Analysis, Speech Synthesis, Sound, Text Animation, Diffusion Models, Image Generation, Quantitative Finance, Object Tracking, Generative Models, ChatGPT, Plugins, GPT-4, OpenAI GPT-3 API, Amazon Textract, Youtube Ads, Videos, LoRa
Amazon EKS, Xcode, Jira, Jupyter, Apache Airflow, AWS ELB, Amazon Elastic Container Service (Amazon ECS), AWS Fargate, Amazon SageMaker
PhD Degree in Computer Science
Nantes Université - Nantes, France
Master's Degree in Artificial Intelligence
ESIEE Paris - Paris, France
Bachelor's Degree in Computer Science
Lebanese University - Beirut, Lebanon
Deep Learning Specialization
DeepLearning.ai | via Coursera
Advanced Machine Learning Specialization
DeepLearning.AI and Stanford Online | via Coursera
Stanford University | via Coursera
iOS Developer Nanodegree