Abdellatif is available for hire
Hire AbdellatifAbdellatif Dalab
Verified Expert in Engineering
Data Scientist and Software Developer
Montreal, QC, Canada
Toptal member since December 8, 2021
Bio
Abdellatif is a machine learning engineer and data scientist passionate about mathematics and applied research. He currently leads the ML engineering initiative at Repustate, where he develops and deploys custom, multilingual deep learning models for various natural language applications. Abdellatif also designs and engineers deep learning API servers to support the company's core natural language services.
Portfolio
Intramove.ai
Amazon EC2, Docker, PyTorch, PyPI, Stripe API, MongoDB, FastAPI, Python, GitHub...
Repustate
Artificial Intelligence (AI), Amazon S3 (AWS S3), Algorithms, Deep Learning...
Decathlon
Python, SQL, Jenkins, Keras, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy...
Experience
- Python - 5 years
- Pandas - 4 years
- Scikit-learn - 4 years
- NumPy - 4 years
- SQL - 3 years
- Natural Language Processing (NLP) - 2 years
- Generative Pre-trained Transformers (GPT) - 2 years
- PyTorch - 1 year
Availability
Part-time
Preferred Environment
Google Colaboratory (Colab), Visual Studio Code (VS Code), Jupyter Notebook, MacOS, GitHub
The most amazing...
...project I've worked on at Repustate is designing and developing a new generation v5 DL microservices API that communicates with a monolith Go-based application.
Work Experience
Founding Engineer
2022 - PRESENT
Intramove.ai
- Engineered core microservices to process payments, register users, keep track of API credits, and perform text analysis.
- Created a PyPI API client for developers to interact with the server.
- Built a website to demonstrate Intramove.ai, an API product for developers interested in analyzing economic news. The core service is powered by SOTA deep learning models for natural language and has endpoints to analyze headlines and articles.
Technologies: Amazon EC2, Docker, PyTorch, PyPI, Stripe API, MongoDB, FastAPI, Python, GitHub, Amazon Route 53
Senior Machine Learning Engineer
2022 - PRESENT
Repustate
- Developed interpretable, supervised deep learning solutions for text using PyTorch and for implementing and improving research paper algorithms, such as BERT, custom attention layers, and hierarchical attention networks.
- Created interpretable, unsupervised deep learning algorithms that eliminated Repustate's need for tagged data when building custom models, enabling scalable growth and taking the company's core service to the next level.
- Designed and developed a new generation gRPC microservices API, allowing the main application developed in Go to communicate with Python deep learning servers.
- Increased inference speed by 2-3x on average for prediction tasks using different techniques, such as ONNX quantization or serving and chunks batching.
- Reduced the turnaround time for onboarding clients with custom needs from an average of 4 weeks to 1-3 days using the new generation API and ML pipeline.
- Built an in-house multilingual transcription service that replaced Amazon Transcribe, reducing annual expenses by 14x.
- Managed a multi-client portfolio, led technical discussions on sales calls, and aided in landing 10+ clients using the new generation API.
- Devised and developed Amazon S3 schemas for production models and tokenizers.
- Delivered custom deep-learning Docker images and RPM packages to be installed in on-premise servers.
- Architected and developed a custom MLflow tracking server to record and monitor experimentation results and artifacts.
Technologies: Artificial Intelligence (AI), Amazon S3 (AWS S3), Algorithms, Deep Learning, Open Neural Network Exchange (ONNX), Hugging Face, gRPC, Protobuf, Git, Go, Python, PyTorch, Keras, MLflow, Amazon EC2, Docker, NumPy, Scikit-learn, Pandas
Data Scientist
2020 - PRESENT
Decathlon
- Developed an in-house data-visualization pipeline that replaced a licensed tool saving $60,000 per year. Used SQL, Git, Jenkins, AWS cloud, Google Sheets API, and Google Data Studio.
- Prototyped an NLU solution with customer reviews classification, keyword extraction, and sentiment analysis that outperformed a licensed tool, saving the marketing team $15,000 per year.
- Created a visual search engine that was deployed as a product retrieval API. It's currently being used for product recommendations.
- Built an unsupervised topic modeling solution for customer reviews with visualization, using sentence transformers. Improved original solution using GPT2 and prompt engineering.
- Developed a store turnover forecasting tool using additive models and custom-made regressors (Prophet API).
- Engineered an NLU product-article recommendation solution as part of Decathlon's personalization strategy.
- Worked on data extraction, transformation, and loading tasks for each solution.
- Made a sustainability reporting tool to monitor the performance of second-life and eco-designed products.
- Built a color detection solution using k-means clustering to aid internal object detection models.
- Interviewed new data science candidates, actively contributed to the hiring process, and mentored new interns on various data-related tasks.
Technologies: Python, SQL, Jenkins, Keras, TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy, Flask, Google Data Studio, Redshift, Amazon S3 (AWS S3), Google Cloud Platform (GCP), GitHub
Machine Learning Intern
2019 - 2019
Decathlon
- Developed and deployed a deep recommendation model for user-click item prediction using LSTM RNN architecture.
- Surpassed the benchmarked precision, recall, and coverage metrics by improving the solution using attention models.
- Developed and deployed an object detection model using TensorFlow's API for a hockey-brand detection application.
Technologies: Python, Algorithms, Keras, SQL, TensorFlow, GitHub
Machine Learning Developer Intern
2018 - 2018
Societe Generale
- Developed a BI reporting tool using MicroStrategy.
- Contributed to data visualization projects using Tableau.
- Helped develop a web application using the Django framework.
Technologies: Tableau, Python, Django, MicroStrategy
Experience
AI Game Project and Report
https://github.com/abdelatifsd/Comp-472---MiniMax-AlgorithmThis project was part of the AI course (COMP 472) at Concordia University. It includes a detailed report of the implementation strategy, minimax algorithm, and outcome analysis. The Jupyter Notebook has most of the code. The project didn't utilize any existing libraries. All algorithms and heuristics were implemented from scratch.
Education
2015 - 2019
Bachelor's Degree in Information Technology
Concordia University - Montreal, Quebec, Canada
Certifications
SEPTEMBER 2020 - PRESENT
Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning
Coursera
JANUARY 2018 - PRESENT
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization, and Optimization
Coursera
JANUARY 2018 - PRESENT
Structuring Machine Learning Projects
Coursera
AUGUST 2017 - PRESENT
Neural Networks and Deep Learning
Coursera
JULY 2017 - PRESENT
Machine Learning with Python in Data Science
Udemy
APRIL 2017 - PRESENT
Machine Learning
Stanford University | via Coursera
Skills
Libraries/APIs
Keras, Scikit-learn, NumPy, Pandas, PyTorch, TensorFlow, React, Protobuf, Stripe API
Tools
Jenkins, GitHub, Tableau, Open Neural Network Exchange (ONNX), Git, PyPI
Languages
Python, SQL, HTML, CSS, JavaScript, Go
Storage
Redshift, PostgreSQL, Amazon S3 (AWS S3), MySQL, Google Cloud Datastore, Database Programming, MongoDB
Frameworks
Flask, Django, gRPC
Paradigms
Database Design
Platforms
Google Cloud Platform (GCP), Visual Studio Code (VS Code), Jupyter Notebook, Docker, MacOS, Amazon EC2
Other
Artificial Intelligence (AI), Data Analysis, Machine Learning, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Google Data Studio, Deep Learning, OOP Designs, Data Structures, Algorithms, Programming Languages, Hugging Face, MicroStrategy, Google Colaboratory (Colab), MLflow, FastAPI, Amazon Route 53
Collaboration That Works
How to Work with Toptal
Toptal matches you directly with global industry experts from our network in hours—not weeks or months.
1
Share your needs
Discuss your requirements and refine your scope in a call with a Toptal domain expert.
2
Choose your talent
Get a short list of expertly matched talent within 24 hours to review, interview, and choose from.
3
Start your risk-free talent trial
Work with your chosen talent on a trial basis for up to two weeks. Pay only if you decide to hire them.
Top talent is in high demand.
Start hiring