Hi, I'm Javier Huang rocket_launch

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

9 experiences

business_center Featured Experience

Data Platform Engineer Intern

Scotiabank arrow_outward Internship May 2026 – August 2026

• Developed self-service onboarding workflows for the enterprise Event Exchange platform within the Scotia Developer Portal (SDP), a Backstage-based developer experience platform built with React, enabling application teams to provision and manage event streaming resources across Confluent Cloud.
• Implemented integration of Event Exchange onboarding into SDP, supporting consolidation of enterprise event-driven capabilities and enabling real-time use cases such as fraud detection, regulatory alerts, Scene+ loyalty processing, and customer notifications.

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 – March 2026

PythonFastAPIOpenRouter APINext.jsReactFlutterAWS EC2GitHub

• Developed AuditMate, an AI-powered web and mobile platform for automating building energy audits at BuildingAssets. Performed data analysis and image preprocessing, and built backend APIs using FastAPI to support audit workflows.
• Integrated OpenRouter API with Google Gemini agents for computer vision-based fixture identification, manual retrieval, and energy improvement recommendations.
• Built Next.js and Flutter frontends, and deployed on AWS EC2, enabling both professional auditors and self-serve clients through automated and guided audit experiences.

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.

21 projects

View most of my projects on GitHub arrow_outward

rocket_launch Featured Projects

Eyas: AI Security Camera Agent arrow_outward

Hugging Face Build Small Hackathon

June 2026 GitHub arrow_outward

PythonYOLO11nMiniCPM-VNemotronllama-cpp-pythonGradioReactTypeScriptViteMUIRechartsFramer MotionComputer Vision

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Eyas HuggingFace Article arrow_outwardSocial Media Post arrow_outward

• Eyas is an offline CCTV intelligence agent that turns raw security footage into a structured event log using a chain of small, locally-running models with no cloud APIs, built to help small business owners detect shoplifting in real time instead of reviewing footage after the fact.
• Runs a four-stage pipeline entirely on CPU: YOLO11n detects and tracks people frame-by-frame; MiniCPM-V 4.6 (1.3B VLM) analyzes sub-sampled crops per tracked person and returns structured JSON observations; a heuristic event structurer converts observations into typed, zone-tagged events with timestamps using configurable evidence buffers; Nemotron 3 Nano 4B (Q4_K_M GGUF via llama-cpp-python) reasons over the structured event log to produce summaries, risk assessments, and natural-language Q&A with grammar-constrained JSON output. TinyAya handles Korean translation and VoxCPM2 generates audio briefings on CUDA-capable machines.
• Built a custom React SPA (Vite, MUI, Recharts, Framer Motion) served as static files through Gradio Blocks, replacing the default Gradio UI with a multi-camera review interface featuring resizable split panels, annotated video playback, a scatter-chart event timeline, and live pipeline progress tracking.
• Social media video filmed at Joy Convenience Store using mock camera angles. Demo footage sourced from publicly available CCTV clips, renamed and run through the full pipeline end-to-end.

Mycelium: Autonomous GitLab Knowledge Continuity Agent arrow_outward

Google Cloud Rapid Agent Hackathon

May 2026 – June 2026 GitHub arrow_outward

PythonFastAPIGoogle CloudVertex AIGeminiGoogle ADKGitLab MCPMongoDBMongoDB MCPMCPDockerCloud RunNext.jsReactTypeScript

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Mycelium Devpost arrow_outward

• Mycelium is a fully autonomous GitLab agent that prevents engineering knowledge loss by continuously modeling ownership, expertise, and structural fragility across a codebase and acting directly inside GitLab to stabilize it.
• Infers real ownership from commits, code reviews, module interaction patterns, and temporal signals. Identifies concentrated expertise risk, orphaned subsystems, and hidden fragility from evolving codebases and forks. When issues are detected, the agent autonomously creates targeted GitLab issues, generates onboarding context packs for new contributors, and produces handoff artifacts from historical activity when engineers go inactive.
• Runs a fully autonomous Observe → Model → Analyze → Decide → Act → Reflect → Persist → Summary pipeline. Webhook-driven triggers (push, merge request, issue, and member events) activate the pipeline in real time. A CLI enables direct operation with live streaming, on-demand onboarding and offboard generation, knowledge graph inspection, and event replay. A Next.js monitoring dashboard provides full visibility into live agent traces, per-stage reasoning, knowledge graph topology, bus factor analytics, and a complete action history log.
• Built on Google Cloud Agent Builder using Gemini via Vertex AI as the reasoning model and ADK as the agent runtime on Vertex AI Agent Engine. Execution layer uses the official GitLab MCP server alongside a custom GitLab MCP server for extended continuity workflows, and MongoDB MCP for persistent graph queries. Backend is a FastAPI server deployed on Cloud Run via Docker, handling webhook ingestion, pipeline orchestration, REST APIs, and SSE streams for real-time UI updates.

Sentinel AML: Real-Time AML Intelligence Platform

2026 Confluent Data Streaming World Tour AI Day Toronto Hackathon, First Place Winner, Most Impactful AI App Award (MacBook Pro Prize)

May 2026

Confluent CloudFlink SQLAzure OpenAIFlink Streaming AI AgentsKafkaPythonFlaskNext.jsData Engineering

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Winner Announcement arrow_outward

• First place winner at the 2026 Confluent Data Streaming World Tour AI Day Toronto Hackathon, with the Most Impactful AI App Award (MacBook Pro Prize). Built Sentinel AML, a real-time anti-money laundering (AML) intelligence platform that continuously analyzes financial transaction streams to detect suspicious activity as it occurs.
• Designed for compliance teams, financial crime investigators, and risk operations units in banks and fintechs, Sentinel AML replaces batch-based compliance with a continuous, autonomous, and auditable decision pipeline.
• Detection runs in Confluent Flink SQL using tumbling-window aggregations and statistical anomaly detection to surface behaviors such as structuring, velocity spikes, and cross-border anomalies within minutes.
• Alerts are enriched via Azure OpenAI (gpt-5-mini) using Confluent's ML_PREDICT function to produce concise analyst-ready explanations inside Flink, and routed to a streaming AI agent that triages cases to escalate, review, or dismiss with recommended Suspicious Activity Report (SAR) decisions.
• The system substantially reduces detection latency and manual investigation workload while improving response speed, operational efficiency, and auditability, lowering false positives compared to batch workflows.

IMI Big Data & AI Competition 2025-2026 arrow_outward

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

December 2025 – April 2026

PythonPandasNumPyscikit-learnJupyter NotebookGitHub

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IMI Big Data & AI Competition 2025-2026 Website arrow_outwardWinning Team Announcement 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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ESC102 Praxis II Course arrow_outwardClover Poster PDF arrow_outwardClover One-Pager PDF 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 standardized via preprocessing and domain normalization so that recordings from different drone units are mapped into the same feature distribution as the training dataset, ensuring compatibility and stable anomaly detection.
• 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.

8 research items

science Featured Research

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

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.

Autonomous Vehicle Path Planning, Deep Learning & Ethics

International Baccalaureate Programme IB Extended Essay November 2023 – February 2025

PythonTensorFlowPyTorch

• Produced a 4000-word research paper evaluating the societal, ethical, and regulatory impacts of autonomous vehicles (AVs) through empirical research and academic literature. Analyzed deep learning applications in AV perception and path-planning systems, assessing both technical capabilities and ethical limitations.
• Designed and conducted a primary survey on public perceptions of AV safety and adoption, generating quantitative insights through data analysis. Synthesized primary and secondary sources to develop evidence-based predictions on future AV regulation and adoption trends.

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

39 certifications

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

47 courses

Academic achievements, competition wins, and recognition for excellence.

19 awards

A breakdown of my technical skills across 6 categories and 129+ technologies.

129+ skills · 6 categories