Hi, I'm Javier Huang rocket_launch

A selection of my professional work experience, roles, and the technologies I've used along the way.

Featured Experience

Cloud Data Platform Engineer Intern

Scotiabank arrow_outward Internship Toronto, Ontario, Canada · May 2026 – August 2026

Team Lead

Scotiabank × IMI BIGDataAIHUB arrow_outward IMI Big Data & AI Competition 2025-2026 December 2025 – April 2026

PythonPandasNumPyscikit-learnJupyter NotebookGitHub

Team Lead of Team 33. Led the team to win 1st place with a $15,000 prize among 430 competitors across 90+ teams, including PhD, Masters, and Undergraduate students.

Engineered a data-intensive, interpretable AI Anti-Money Laundering (AML) system with machine learning models across individual and small business entities, using customer-level features across multiple transaction types in largely unlabelled financial data.

Designed a source-compliant AML knowledge library (FINTRAC, FINCEN, FLSC, etc) mapping regulatory red flags to data features via AML patterns, enabling SHAP-generated, LLM-enhanced explanations with auditability and factual consistency.

Built a full-stack Next.js web application integrating detection models, explainability, and the AML knowledge library into an investigator-facing workflow platform.

Project Manager & Architect

Agentiiv arrow_outward Multi-Agent AI Startup December 2025 – March 2026

MCPFastAPINext.jsReactPostgreSQLPrometheusGrafanaDockerJiraGitHub

Project Manager and Architect for Agentiiv's MCP Gateway, a production-grade orchestration layer enabling secure AI agent to Model Context Protocol (MCP) server communication.

Defined system scope, architecture requirements, and delivery milestones while coordinating development using Agile Scrum and Jira. Designed and deployed a containerized AWS-hosted gateway integrating 5 MCP servers (134 tools total), including PostgreSQL, Slack, and Google Workspace via a unified MCP protocol.

Implemented core backend infrastructure including deterministic routing, JWT SSO authentication, RBAC access control, centralized logging (PostgreSQL), and rate limiting.

Built platform reliability and observability systems including Prometheus and Grafana monitoring, plus a React dashboard for real-time visibility into request traffic, server usage, system health, and failures; enabled multi-agent workflow execution through gateway-mediated tool access.

Software & Machine Learning Engineer

BuildingAssets arrow_outward AI Energy Auditing Startup October 2025 – April 2026

PythonPyTorchTensorFlowOpenCVPaddleOCRAWS EC2SupabaseNext.jsReactFlutterGitHub

Developing AuditMate, an AI-driven web and mobile platform that automates building energy audits for BuildingAssets. The application analyzes energy audits with object detection, OCR, and LLMs.

Performing data analysis and image preprocessing. Building and integrating machine learning and deep learning pipelines that support both professional auditors and individual clients, enabling self-serve and guided audit experiences.

Deploying the full cloud-based architecture using AWS EC2 and integrating AI services with Next.js and Flutter frontends to deliver scalable, accessible applications.

Technology Director

GenAI Genesis arrow_outward October 2025 – March 2026

Next.jsReactSupabasePostgreSQLREST APIsZodJestFigmaGitHub

Full-stack developer and organizer of GenAI Genesis 2026, Canada's largest AI hackathon with 2000+ applicants, 800+ hackers (30% increase from 2025), 250+ projects, and 90+ judges. Built and maintained the website, as well as participant and judging platforms, which supported 10,000+ uses during the hackathon.

Designed and implemented secure REST APIs with Zod validation, database schemas, role-based access control, and user interfaces, as well as CI/CD pipelines with GitHub Actions and Vercel for scalable deployment. Collaborated with cross-functional teams, delivering a seamless hackathon experience.

Explore my projects across three categories: Machine Learning & AI, Business & Education, and Online Web Games.

View most of my projects on GitHub arrow_outward

Featured Projects

IMI Big Data & AI Competition 2025-2026 arrow_outward

1st Place Winner with a $15,000 prize - Hosted by IMI BIGDataAIHUB in partnership with Scotiabank

December 2025 – April 2026

PythonPandasNumPyscikit-learnJupyter NotebookGitHub

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IMI Big Data & AI Competition 2025-2026 Website arrow_outward

Team lead of team 33. Led the team to win 1st place with a $15,000 prize among 430 competitors across 90+ teams, including PhD, Masters, and Undergraduate students, in the IMI Big Data & AI competition hosted by the Institute for Management & Innovation (IMI) UTM BIGDataAIHUB in partnership with Scotiabank.

Engineered a data-intensive, interpretable AI Anti-Money Laundering (AML) system, with data analysis pipelines using 16 machine learning models across individual and small business entities, trained on customer-level features aggregated over 7 transaction types plus an all-types view in largely unlabelled financial data, capturing overall and transaction-specific anomalies.

Designed and developed a source-compliant AML knowledge library (FINTRAC, FINCEN, FLSC, etc) mapping regulatory red flags to data features via AML patterns, enabling SHAP-generated, LLM-enhanced explanations that are human-readable, distinguish fraud vs. AML, and preserve auditability and factual consistency for compliance use.

Built a full-stack Next.js web application integrating detection models, explainability, and the AML knowledge library into a unified investigator-facing workflow platform.

Clover: Drone Propulsion System Health Diagnostics

U of T ESC102 Praxis II Course Project

January 2026 – April 2026

PythonPyTorchSupabaseFlutterNext.jsReactNumPyPandasMatplotlibJupyter NotebookGitGitHub

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Clover Poster arrow_outwardClover One-Pager arrow_outward

Built Clover, a portable AI-powered pre-flight diagnostic system for D. Vision Aerials' FPV drones. Clover replaces manual motor spin checks with automated go/no-go assessments, improving consistency in pre-flight decisions and reducing operational risk in dense urban environments.

The system evaluates drone health by combining acoustic motor analysis with onboard flight controller blackbox telemetry, comparing expected motor behavior against observed performance to detect and localize faults prior to flight.

Clover runs on an edge platform (Raspberry Pi), producing real-time, time-stamped diagnostic outputs with full audit logs, and syncing results to a Supabase cloud database. Data is accessible remotely through a Flutter mobile app and a Next.js web dashboard for monitoring and review.

For acoustic fault detection, it uses a multitask 1D CNN-ResNet architecture based on sound classification research. The deep learning model identifies fault types (motor, propeller) and infers flight direction. Audio inputs are preprocessed and mapped to the format of the training dataset for compatibility. The model was trained for 100 epochs on 324k audio samples, reaching 97.4% accuracy and F1 score on the reference dataset.

In field testing, Clover achieved 81% accuracy/F1 on acoustic data and 87% on blackbox telemetry-based diagnostics.

SceneClarity: A Unified Framework for Scene Reliability Estimation and Classification in Autonomous Vehicle Perception

UTMIST Machine Learning Project

August 2025 – April 2026

PyTorchTensorFlowscikit-learnPandasNumPyJupyter NotebookGoogle ColabYOLOv11MobileNetV3ResNet-50LaneNetDockerREST APIsNext.jsReactGitHubVisual Studio CodeJira

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UTMIST SceneClarity Presentation arrow_outward

Led the development of the SceneClarity ML project, a modular framework for estimating scene-level reliability in autonomous vehicle perception, addressing degradation under adverse conditions such as fog, rain, snow, and glare where failures often co-occur and are difficult to diagnose at the system level.

The architecture separates perception, environmental inference, and aggregation modules through a fixed interface, allowing components to be replaced without redesigning the aggregation logic.

Introduces a framework that aggregates perception outputs and environmental signals into a global reliability score with attribution to likely degradation factors, representing reliability as a decomposition over semantically interpretable scene-level components, unlike per-prediction uncertainty methods.

Implemented as a real-time system producing structured outputs and visualizations to support failure analysis, safety monitoring, and debugging.

Academic and independent research spanning AI & ML systems, physics, chemistry, and engineering.

Featured Research

Vision Transformer (ViT-B/16) Architecture Implementation arrow_outward

Independent Independent Research December 2025 – January 2026 GitHub arrow_outward

PythonPyTorchTorchvisionTorchinfoNumPyMatplotlibPILKagglehubJupyter NotebookGoogle ColabGitGitHub

Implemented the Vision Transformer (ViT-B/16) architecture from scratch in PyTorch, following the paper "An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale." Manually built all core components, including convolutional patch embeddings, class and positional embeddings, Multi-Head Self-Attention (MSA) and MLP blocks with Layer Normalization (LN) and residual connections, as well as the final classification head.

Used the equations and architectural definitions from the original paper to reason about data flow and tensor transformations throughout the model, explicitly tracking tensor shapes step-by-step from input images to output classification in order to ensure correctness and deepen understanding of the model structure.

Validated the implementation end-to-end by training the model from scratch on a 5-class weather image classification dataset sourced from Kaggle. Documented training simplifications relative to the paper and compared the custom implementation with PyTorch's built-in ViT.

Canadian Young Physicists' Tournament arrow_outward

Team Schwarzschildren Physics Competition November 2022 – March 2024

PythonMatlabMatplotlibCADAutodesk Fusion 360

As a senior member of team Schwarzschildren, conducted in-depth theoretical and experimental research on open-ended physics problems, winning a gold medal in 2023 and a bronze medal in 2024. Researched various physics topics including waves, acoustics, electromagnetism, collision theory, beam theory, fluid dynamics, and optics.

Developed simulations, designed experimental setups, and collected experimental measurements; conducted data cleaning and statistical analysis, interpreting results to validate physical models using MATLAB and Python.

Constructed research reports, presented, and defended findings in formal physics debates, demonstrating strong scientific reasoning and communication.

Featured certifications across AI/ML, cloud platforms, databases, and more technologies.

Rigorous academic coursework from University of Toronto Engineering specializing in Software Engineering, Data Science, and Artificial Intelligence.

Academic achievements, competition wins, and recognition for excellence.

A breakdown of my technical skills across 6 categories and 136+ technologies.