Raed Abdel-Sater

Senior AI & ML Computational Science Manager

17+ Years • GenAI & LLMs • Federated Learning • MLOps

Leading applied AI and machine learning initiatives across Fortune 500 companies. Specialized in GenAI, LLMs, federated learning, and end-to-end MLOps frameworks. Top 5% Data Scientist at Accenture and member of the Data Scientist Elite group.

$16M+

Sales Pipeline Generated

45%

Revenue Growth

35%

Productivity Increase

$3.6M

Cost Savings Delivered

View Research GitHub Profile

Professional Experience

Senior AI & ML Computational Science Manager

Accenture • Montreal, Canada

Jun 2025 - Present

                            
                            LLM Solutions: Architected end-to-end LLM applications on Databricks using DBRX, achieving 45% productivity increase in ticket resolution
                        

                            
                            Agentic AI: Built multi-agent systems with LangChain's LangGraph and AutoGen for complex AI task orchestration
                        

                            
                            Revenue Impact: Generated $13M in sales pipeline and lifted division revenue from 39% to 45%
                        

                            
                            Computer Vision: Applied advanced prompt engineering with Stable Diffusion, improving defect detection by 30%
                        

Data Science & Machine Learning Manager

Accenture • Montreal, Canada

Aug 2022 - May 2025

                            
                            MLOps Framework: Implemented end-to-end MLOps for traditional ML models, enhancing deployment by 30%
                        

                            
                            Government Projects: Led multimillion-dollar Databricks platform modernization for Canadian government
                        

                            
                            Team Leadership: Managed multiple teams, mentoring staff and building high-performance practices
                        

Data Science Manager

Techo-Bloc • Montreal, Canada

Apr 2021 - Aug 2022

                            
                            Anomaly Detection: Developed deep learning systems using YOLO and LSTM, reducing production costs by 30%
                        

                            
                            Team Building: Established cross-functional data science team promoting collaboration and innovation
                        

Senior Data Scientist & Team Lead

General Electric • Montreal, Canada

Sep 2019 - Dec 2020

                            
                            Energy Optimization: Achieved $3.6M ROI through energy consumption prediction models across 3960 buildings
                        

                            
                            Big Data Solutions: Implemented solutions using Cassandra, HDFS, S3 for large-scale data processing
                        

Featured Research & Publications

Published • arXiv 2024

🚀 FedTime: Federated LLM for Time Series Forecasting

First federated learning framework using Large Language Models for long-term time series forecasting. Achieves up to 20% improvement over state-of-the-art while preserving data privacy through innovative K-means clustering and parameter-efficient fine-tuning.

arXiv Paper Code Repository

Preprint • 2025

🔗 BLEND: Blockchain-Enhanced Federated Learning

Novel blockchain-enhanced network for decentralized federated learning of LLMs for energy prediction in Internet of Vehicles (IoVs). Combines privacy-preserving ML with distributed ledger technology.

Coming Soon

Published • arXiv 2021

🏢 Federated Learning for Smart Buildings

Comprehensive approach to anomaly detection in smart buildings using federated learning, preserving privacy while enabling collaborative learning across building management systems.

arXiv Paper

Published • Taylor & Francis 2022

📊 Marriage Dissolution: Survival Analysis

Advanced statistical analysis applying survival analysis techniques to understand marriage dissolution patterns, published in a peer-reviewed journal.

Journal Paper

Technical Expertise

Raed Abdel-Sater | ML Engineer & Researcher

ML Engineer & Researcher

Federated Learning • Time Series • LLMs

Pioneering privacy-preserving machine learning solutions for real-world applications. Specialized in federated learning frameworks and large language models for time series forecasting.

View Research GitHub Profile

Featured Research

🚀 FedTime: Federated LLM for Time Series

First federated learning framework using Large Language Models for long-term time series forecasting. Achieves 20% improvement while preserving data privacy through innovative K-means clustering and parameter-efficient fine-tuning.

arXiv Paper Code

🔒 Privacy-Preserving ML Systems

Developing next-generation federated learning algorithms for sensitive domains including healthcare, finance, and smart cities. Focus on differential privacy and secure aggregation protocols.

In Progress

⚡ Edge AI Optimization

Optimizing deep learning models for deployment on resource-constrained edge devices. Research includes model compression, quantization, and efficient neural architecture search.

Coming Soon

Technical Expertise

Machine Learning

PyTorch TensorFlow Scikit-learn Transformers LLMs

Federated Learning

PySyft FedAvg Differential Privacy PEFT LoRA/QLoRA

Time Series

Forecasting ARIMA Prophet Transformers Anomaly Detection

Engineering

Python Docker Kubernetes AWS MLOps

Let's Connect

Interested in collaboration or discussing research opportunities?

raed.abdelsater@mail.concordia.ca GitHub LinkedIn Google Scholar