Muhammad Abdullah

Computer Engineering @ UofT | ML Infrastructure & AI Agentic Systems | Saved $152K+ in industrial ML optimizations

Based in: London, ON, CanadaMost recently: Machine Learning Engineer (Intern), Great Lakes CopperExperience: 1 year

Computer Engineering student at the University of Toronto with a track record of deploying production-grade ML infrastructure. From saving $152K in manufacturing rejections at Great Lakes Copper to architecting safety-first AI agents with 768-dim semantic memory, I specialize in bridging the gap between complex algorithms and high-performance, real-world applications.

Experience

Machine Learning Engineer (Intern)

Great Lakes CopperLondon, ON, CanadainternshipJun 2025 – Jun 2026

higher top-quality tube yield: 16%higher top-quality tube yield
better average eccentricity: 2%better average eccentricity
saved in lower eccentricity rejections: $152Ksaved in lower eccentricity rejections

Engineered production ML infrastructure across two live systems - GPU-optimized inference pipelines with lazy model loading deployed on Google Cloud Run, cutting cold-start memory 30%. Architected and developed an ML-powered RAG based incident retrieval platform semantically matching against 100k+ records, achieving 90%+ top recommendations accuracy, and a targeted 40% MTTR reduction. Built an ML Eccentricity Recommendation System optimizing press parameters 16% higher top-quality tube yield, 2% better average eccentricity, $152K saved in lower eccentricity rejections. Owned product discovery for an open-ended AI initiative to reduce production defects - defining scope, success metrics, and a phased roadmap through VP-level stakeholder alignment.

MLGPUGoogle Cloud RunRAGSemantic Matching

AI Intern

Dr. Amir Ahmad, United Arab Emirates UniversityAbu Dhabi, UAEinternshipMay 2023 – Aug 2023

Benchmarked 4 anomaly detection algorithms (LOF, KDE, Isolation Forest, OCSVM) on one-class classification and imbalanced datasets, identifying the top performer at 67.4% ROC-AUC across 4 dataset samples. Designed a train-test-validation evaluation framework in Python, analyzing ROC-AUC and precision-recall metrics to characterize each algorithm's strengths and failure modes across diverse data distributions. Built reproducible ML experimentation workflows using Scikit-Learn, PyOD, NumPy, pandas, and matplotlib for preprocessing, training, and results visualization.

LOFKDEIsolation ForestOCSVMPythonScikit-LearnPyODNumPypandasmatplotlib

Skills

Technical

Python

Git

GitHub Copilot

Cursor

Tensorflow

Pandas

REST API

C++

Scikit-Learn

Cloud Run

GCP

Numpy

RAG

Semantic Search

Vector Embeddings

Pytorch

Agile

JavaScript

HTML5

CSS3

SQL

Claude Code

Gemini CLI

FastAPI

React

WebSockets

SQLite

BigQuery

GCS

Docker

CI/CD

JWT

SDLC

MLflow

Codex

Swift

Zustand

Vite

GNNs

Kubernetes

Duo SSO

Optuna

Projects

Urban Planner Mapping Application

Developeracademic

Developed a C++ map application for urban planners using the OSM/Streets Database API, GTK, and EZGL libraries to render maps, features, street names, and POIs. Reduced debugging time by 25% through Git version control and unit testing, and boosted performance by 35% using STL data structures and a multi-threaded Dijkstra algorithm for faster shortest-path computation.

C++OSM APIStreets Database APIGTKEZGLGitSTLDijkstra algorithm

Flappy Bird

Programmeracademic

Programmed a Flappy Bird game in C with VGA-based board rendering, FPGA-driven score display, and PS/2 keyboard input for bird control and player switching via an instructions menu. Employed pass-by-reference for large C arrays storing game images, avoiding unnecessary copies to reduce memory overhead and improve rendering performance.

CVGAFPGAPS/2

Traffic Sign Recognition

Tester/Developerpersonal

Designed and executed test cases for an ANN traffic sign classifier functional and regression testing across a 35,000+ image data loader and 40+ classes, resolving class-imbalance and edge-case defects to maintain 85% validation accuracy consistency. Built an automated CI/CD testing environment for the ANN-to-CNN pipeline, collaborating with developers and PMs to accelerate defect identification and resolution.

ANNCNNCI/CD

Personal-Use AI Agent

Architectpersonal

Architected a safety-first macOS AI agent (hexagonal Python core, custom LLM orchestration layer, 12 sandboxed tools, 3 memory modes); grew test coverage 340 -> 599 (+76%) while cutting tool attack surface 21 -> 12 (-43%). Built a hybrid lexical/vector semantic memory system (768-dim Gemini embeddings, LRU caching) with a local privacy-preserving NLP intent classifier, served through a native Swift <-> Python WebSocket interface.

PythonLLMGemini embeddingsSwiftWebSocketsNLP

Real-Time Sign Language Recognition

Developerpersonal

Developed a real-time sign language detection system using TensorFlow/Keras LSTM models, achieving low-latency inference snappy enough for natural signing speeds. Executed test plans for the video preprocessing pipeline frame extraction, normalization, and feature engineering. Evaluated LSTM action-recognition on a live camera feed with OpenCV test environments, measuring spatial-temporal accuracy and logging misclassifications for performance tuning.

TensorFlowKerasLSTMOpenCV

Education

BASc. in Computer Engineering, 2T6+PEY

University of TorontoComputer EngineeringSep 2022 – Apr 2027

Coursework

Operating Systems

What drives the work

Motivations: Building high-performance AI infrastructure
Solving complex industrial problems with ML
Architecting safety-first agentic systems

Hire MuhammadReach out about a role, a contract or a conversation.For recruiters

Muhammad's twin is AI, it can make mistakes.

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