I'm Eric, a Software Engineering graduate from McMaster who doesn't just pick a lane. AI pipelines, REST APIs, mobile apps, neural networks: if it needs building, I build it.
I work across the entire stack — from training neural networks on 100k+ samples to designing the interface that delivers the result.
What I Bring
AI & Machine Learning
PyTorch neural networks on 100k+ samples, OpenAI Whisper pipelines, LLM integrations. I build AI that actually makes it to production, not just a notebook.
Software Engineering
Scalable REST APIs, authentication systems, offline-first architectures, CI/CD, testing. The engineering fundamentals that make software reliable and maintainable.
Product & Interface
Cross-platform React Native apps with Expo, from wireframe to polished product. I care about how users interact with what I build, not just what runs behind the scenes.
Skills
What I have worked with in practice.
Languages
Python
Java
C++
JavaScript
HTML/CSS
TypeScript
C
SQL
Frameworks & Libraries
PyTorch
FastAPI
Flask
React Native
NumPy
Pandas
scikit-learn
Tools
Docker
AWS EC2
Render
Claude Code
GitHub Actions
JUnit
SQLite
Projects
Don't take my word for it. See what I've built.
Pocket AI
CATTLElytics Inc. Featured on the official McMaster Engineering Instagram
Sep 2025 - Apr 2026
Speak naturally, get structured tasks. End-to-end voice-to-task system built with Whisper, an LLM pipeline, Flask API, and React Native.
PythonFlaskReact NativeOpenAI WhisperREST API
Integrated OpenAI Whisper for real-time audio transcription, validated through usability testing that achieved 100% system voice recognition accuracy under simulated barn noise, exceeding the 95% project target.
Built a Flask backend powering voice-to-task workflows with REST APIs for authentication, task lifecycle management, and synchronization with CATTLElytics' proprietary storage system.
Implemented an offline-first architecture with local SQLite storage and background syncing, ensuring reliable performance in low-connectivity farm environments.
Designed and built mobile UI components in React Native (Expo), including task views, audio capture flows, and speech confirmation feedback; usability testing yielded a mean satisfaction score of 4.4/5.
WATCH DEMO
Wildfire Risk Classifier
Sep 2025 - Jun 2026
Neural network trained on 118,000+ NASA satellite samples. Classifies wildfire risk with 94.3% accuracy, beating every baseline we tested — now deployed as a live REST API.
PythonPyTorchFastAPIDockerAWS EC2
Deployed a production REST API using FastAPI and Docker on AWS EC2, serving real-time wildfire risk predictions via containerized cloud infrastructure.
Developed a neural network to classify regions into low, medium, and high wildfire risk using 118,000+ samples from NASA FIRMS and Open-Meteo datasets.
Designed a 4-layer MLP with batch normalization, dropout regularization, and weighted cross-entropy loss to address class imbalance across risk categories.
Achieved 94.3% test accuracy with high F1-scores across all classes, outperforming Random Forest and SVM baselines by 15%.
SneakPeek
Jan 2025 - Mar 2025
Point your phone at any sneaker and get an instant AI identification plus purchase links. Custom DenseNet201 model, multi-agent Blackboard architecture, React Native app.
PythonPyTorchReact NativeJavaScript
Built a full-stack mobile app for AI-powered sneaker identification and social forum, where users upload images and receive an AI-identified shoe model, brand, and purchase links.
Trained a custom DenseNet201 model on a labelled dataset, applying a data augmentation pipeline (perspective transforms, saturation jitter, geometric transforms) to maximize generalization.
Architected a multi-agent Blackboard system where Google Gemini 2.0 Flash, GPT-4o, and the custom PyTorch model each submit predictions to a shared space; a central Gemini controller arbitrates and selects the final result.
How a prediction is made
Sneaker photo uploaded
Gemini 2.0 Flash
GPT-4o
DenseNet201custom PyTorch
3 agents predict in parallel
Blackboard · shared prediction space
Gemini controller arbitrates → best match
Brand, model & purchase links
Autonomous Drone Rescue
Sep 2023 - Dec 2023
Autonomous flight software that navigates procedurally generated islands to locate and rescue targets. Built in Java with a team of three.
JavaJUnitMavenGit
Developed autonomous flight software to locate survivors across unknown terrain, implementing algorithms for optimal pathfinding to systematically cover the area.
Designed and executed test cases with JUnit to verify functionality of software components across a three-person team.
Background
Education & Experience.
McMaster University
Hamilton, ON
Degree
B.Eng Software
cGPA
3.70 / 4.0
Started
2022
Graduated
May 2026
Relevant Courses
AI / Machine Learning
Applications of Machine LearningData MiningProbability & Statistics
Signals and SystemsComputer Networks & SecurityLinear Optimization
Software Engineer
Sep 2025 - Apr 2026
CATTLElytics Inc. · Hamilton, ON
Led AI development for Pocket AI, a voice-driven task management app supporting dairy operations for 1 in 50 cows across North America.
Directed a team of 5 engineers through the full product lifecycle — from requirements and architecture to production handoff, delivering an app being integrated into CATTLElytics' platform.
Integrated OpenAI Whisper for real-time audio transcription, achieving 100% voice recognition accuracy under simulated barn noise, exceeding the 95% project target.
Built a Flask backend with REST APIs for authentication, task lifecycle management, and sync with CATTLElytics' proprietary storage system.
Implemented an offline-first architecture with local SQLite storage and background syncing for reliable performance in low-connectivity farm environments.
Coordinator, Impact Initiative
Mar - Apr 2023
McMaster University
Collaborated with a team of Engineering students to design an adaptive kitchen tool for individuals with visual impairments.
Contact
Let's build something great together.
Graduated May 2026 and ready for what's next. Full-time roles or projects that need an engineer who builds across the stack. Let's talk.
