Hello, I'm

Feras

I am aGenAI Developer
headshot

My Profile

I am a final-year Computer Science student at Sheridan College specializing in GenAI and ML development. Currently, I serve as an ML Developer and Research Assistant, leading the deployment of machine learning services that support over 20,000 active users. I am particularly driven by engineering data pipelines that transform manual verification into automated, scalable workflows.

Outside work, I play soccer and love experimenting with new recipes in the kitchen.

Food hobby
Soccer hobby

Work Experience

AI/ML Developer

Sheridan College
Oakville, Ontario
Oct 2025 – PRESENT
  • Platform & Data Governance: Architected and governed highly scalable data schemas, ingestion pipelines, and automated validation workflows, specifically supporting productionized RAG Agents and sophisticated NLP systems.
  • CI/CD & Reproducibility (Model Lifecycle): Designed and operationalized the end-to-end Machine Learning lifecycle (MLOps), transforming prototype ML engines into highly reproducible CI/CD pipelines that fully automate feature engineering, model training, and continuous evaluation.
  • High-Volume Production Operations: Led the deployment, monitoring, and sustained operation of high-impact, containerized ML services, managing infrastructure reliability and scaling to support 20,000+ active users with 99.9% up-time and volume requirements.
  • Cross-Functional Project Lead: Collaborated with designers and developers, defining operational requirements and implementing best practices to ensure models, vector stores, and data flows were inherently scalable, maintainable, and cost-efficient in production environments.

Data Analyst

Paradigm Electronics Inc.
Mississauga, ON
May 2024 – Aug 2025
  • Data Pipeline Engineering: Optimized Airflow ETL DAGs, achieving a runtime reduction from 12 minutes to 8 minutes through parallel processing for automated daily, monthly, and yearly reporting for Global Sales Operations.
  • Business Intelligence & Financial Impact: Developed and deployed Key Performance Indicator (KPI) dashboards for Production and Engineering teams, highlighting products with the highest quality risks to drive operational efficiency and reduce defect-related costs.
  • Predictive Demand Modeling: Streamlined distribution planning by forecasting 6-month product demand across all SKUs using 3 years of historical data, powered by PySpark distributed regression.
  • Cloud Infrastructure & Automation: Deployed Node.js/EJS apps on AWS ECS achieving 99.5% uptime; optimized GitHub Actions CI/CD pipelines with IaC, reducing deployment time from 15 to 8 minutes.

Top Projects

Completed

HR / Talent Acquisition App

GitHub ↗View Demo ▶

Smart Recruitment Platform that streamlines the hiring process by automatically analyzing resumes and verifying candidate backgrounds.


The system employs Autonomous AI Agents that act like digital recruiters. They use web browsing tools to verify candidate claims, cross-reference experience on external sites, and conduct research to present a validated report to hiring managers.


Built for scale and reliability, the system features a full observability stack with Prometheus (metrics),Grafana (dashboards), and Tempo (distributed tracing). The infrastructure is fully containerized withDocker (featuring "baked-in" models) and powered by MongoDB for event-driven persistence.


Deployed on: Microsoft Azure

Key Technical Highlights
  • Micro-Service Architecture: Decoupled agents using Model Context Protocol (MCP).
  • Production Observability: Full metric tracking with Prometheus & Grafana.
  • Containerization: Optimized Docker builds with pre-loaded models for performance.
  • Event-Driven Design: Asynchronous pipelines triggered by database events.
Next.jsReactTypeScriptLangChainLangGraphLlama 3LlamaParsePuppeteerMongoDBAzure Container AppsGitHub ActionsOpenTelemetry
Completed

OptiMulti-Video

GitHub ↗

High-Performance Multimodal Attention with Custom CUDA Kernels. This project demonstrates a vertical slice of a high-performance Multimodal AI system.


It features a Custom CUDA Kernel written in CUDA C++ for low-latency fusion of video and text embeddings, integrated into a compact Video-Text Transformer architecture.


The training pipeline utilizes Fully Sharded Data Parallel (FSDP) to distribute workloads across dual T4 GPUs, optimizing for both performance and resource accessibility.


Tested on: Google Colab / Kaggle (Dual T4 GPUs)

Key Technical Highlights
  • Custom CUDA Kernel: Fused "Normalize & Project" for low latency.
  • Distributed Training: FSDP implementation for multi-GPU setups.
  • Multimodal Fusion: Video-Text Transformer architecture.
CUDA C++PyTorchFSDPPythonGoogle ColabTransformer
Completed

Sports Analytics App

GitHub ↗

Production-Grade Computer Vision Application developed to analyze cricket shot mechanics in real-time. This mobile platform utilizes YOLOv8 for high-speed object detection to track ball trajectories and RandomForest classifiers to provide instant, data-driven shot recommendations and corrective feedback to players.


The system features a cross-platform mobile app built with Flutter, offering a seamless user experience for recording execution and receiving detailed coaching insights. Inference is decoupled via a serverless architecture on Google Cloud Platform (Cloud Run), ensuring cost-effective scaling to zero when idle, while Pub/Sub handles asynchronous event messaging for smooth analysis workflows.


Deployed on: Google Play Store (Internal Testing) & GCP

Achievement
  • First Place for Capstone projects in data analytics stream.
Impact Metrics
  • Deployed to production with ~10 active users.
  • Real-time CV inference via serverless architecture.
  • Cost-optimized ML inference using Cloud Run.
Key Contributions
  • Designed end-to-end CV pipeline using YOLOv8 to detect ball trajectories.
  • Deployed scalable inference using Cloud Run.
PythonOpenCVYOLOv8RandomForestFlutterGoogle Cloud PlatformCloud RunPub/Sub

Endorsements

Tom Khirdaji

Information Technology Manager at Paradigm | Anthem | Martin Logan Electronics

"I had the pleasure of supervising Feras Mahmood during his 16-month internship as a Data Analyst at Paradigm. Over this time, Feras consistently demonstrated strong technical skills, curiosity, and a commitment to learning. Feras worked on a wide range of projects, delivering KPI dashboards, writing SQL queries, and integrating data through RESTful APIs. He also contributed to automation efforts using PowerShell scripts, which streamlined some of our internal workflows. Beyond analytics, Feras showed initiative by building several web applications using JavaScript, Node.js, and React, expanding his impact beyond traditional data analysis. What stood out about Feras was his ability to quickly grasp new tools and technologies, and his willingness to take ownership of tasks. His contributions helped our team improve data visibility and operational efficiency. I am confident that his blend of analytical and development skills will serve him well in his future career, and I would gladly recommend him for any data or technology role he pursues."

Tom managed Feras directlySeptember 5, 2025

Volunteering

Workshop Team Member

Google Developer Group Sheridan
May 2025 – Present
  • Supported and organized the design and delivery of community tech workshops, helping 100+ attendees gain hands-on experience with Google technologies.

Co-Organizer

Datathon (Google Developer Group)
Oct 2025
  • Co-organized a 300+ people hackathon 'Datathon' with Google Developer Group, managing logistics and mentor support.
  • Event Link ↗

Contact

Feel free to reach out to me via email or LinkedIn.
technocratz979@gmail.com →