(Sep 2024 - October 2025)
Software Engineer
Laser Quantum
constengineer={name:'Charbel Barakat',skills:['Python', 'C++', 'Bash', 'FastAPI', 'SQL', 'Docker', 'Linux', 'Git', 'Machine Learning', 'Deep Learning', 'NLP / LLMs', 'PyTorch', 'TensorFlow', 'Data Pipelines', 'MLOps', 'REST APIs', 'Cloud & AWS', 'Networking', 'Embedded Systems'],hardWorker:true,quickLearner:true,problemSolver:true,hireable:function() {return(this.hardWorker&&this.problemSolver&&this.skills.length>=5);};};ABOUT ME
About Me
I am an engineer with experience building and deploying data-driven and software systems across machine learning, full-stack development, and networked environments. I have worked on projects ranging from ML model development and evaluation to production automation, embedded systems, and cloud-based services. With a background in electrical engineering and applied artificial intelligence, I bring a strong foundation in software design, systems thinking, and problem-solving. I enjoy working at the intersection of data, software, and infrastructure, and I am motivated by building reliable, scalable solutions through thoughtful engineering and cross-functional collaboration.
Experiences
(May 2024 - Sep 2024)
Network Engineer Intern
Videotron
(May 2023 - Sep 2023)
Software and Electronics Development Intern
Laser Quantum
(April 2021 - Feb 2023)
Infantry Soldier (Reservist)
Canadian Armed Forces
Skills
PROJECTS
Movie Recommendation System (MLOps)
constproject={name:'Movie Recommendation System (MLOps)',tools: ['GitLab', 'Kafka', 'Pytorch', 'Flask', 'Pandas', 'Docker', 'AWS S3', 'AWS IAM', 'Grafana', 'Prometheus', 'Nginx],myRole:Data Pipeline and Cloud Architecture,Description: My team and I built an end-to-end movie recommendation system using a hybrid collaborative filtering Multilayer Perceptron model with cold-start handling and ranking-based evaluation using Precision@K, Recall@K, NDCG@K, and Hit Rate@K. The system was deployed as containerized microservices with Docker Compose, supporting canary rollouts (80/20 traffic split), Kafka-based stream processing, and monitoring with Prometheus and Grafana on AWS. We automated data ingestion, preprocessing, evaluation, and deployment using GitLab CI/CD to ensure reproducible and production-ready MLOps workflows.,};InftyThink With Cross-Chain Memory
constproject={name:'InftyThink With Cross-Chain Memory',tools: ['OpenAI API', 'Jupyter', 'Matplotlib', 'Git],myRole:Simulation Setup and Refinining,Description: My team and I worked on a memory-augmented LLM reasoning system that extends InftyThink with an embedding-based cache to reuse successful reasoning steps during inference. The system improves accuracy on structured benchmarks while exposing scalability and robustness limits in heterogeneous domains. This project emphasizes evaluation pipelines, retrieval design, and practical trade-offs in deploying reasoning-enhanced LLM systems.,};Bitcoin Trading Agent with Take Profit & Stop Loss
constproject={name:'Bitcoin Trading Agent with Take Profit & Stop Loss',tools: ['Docker', 'Pytorch', 'Pandas', 'CCXT API],myRole:,Description: I developed a supervised learning time-series classification system using a Temporal Convolutional Network (TCN) in PyTorch to predict discrete market actions on Bitcoin/USDT data. The project includes a realistic backtesting and evaluation pipeline with position management, transaction cost modeling, and risk-aware metrics such as Sharpe ratio and maximum drawdown. An automated data ingestion and experiment pipeline enables reproducible training, evaluation, and visualization through Dockerized workflows.,};Network Packet Classifier
constproject={name:'Network Packet Classifier',tools: ['Pandas', 'WireShark', 'Jupyter', 'Sci-kit learn],myRole:Data Collection, Preprocessing and Analysis,Description: My team and I built a modular machine learning pipeline to classify network traffic from Ethernet and Wi-Fi packet captures using statistical feature extraction. The system processes large packet-level datasets and applies supervised learning models to perform application-level classification across services such as YouTube and Zoom. The project emphasizes scalable preprocessing, reproducible experimentation, and robust evaluation across diverse traffic sources.,};Education
2025 - 2026
Master of Engineering - Applied Artificial Intelligence
McGill University
2020 - 2024
Bachelor in Electrical Engineering
École Polytechnique de Montréal