Robot Vision Engineer & AI Researcher based in Ottawa, Ontario, Canada.
Robot Vision Engineer specializing in SLAM, computer vision, and drone-based 3D mapping systems. Expertise in AWS, Docker, Python, and embedded systems. Constantly working on projects to push the boundaries of autonomous systems and AI.
Currently working as a Researcher at Carleton University developing real-time indoor 3D mapping systems using consumer drones, cloud-based SLAM, and AR visualization. Previous experience includes DevOps engineering at Magnet Forensics and spectrum engineering at Telesat.
Master of Applied Science - MASc Carleton University Electrical and Computer Engineering January 2024 - November 2025
Thesis: Indoor 3D Modeling Using Consumer Drones and Neural Simultaneous Localization and Mapping (SLAM) for Virtual Reality and a Cloud Architecture. Defending November 2025.
Bachelor of Engineering - BE Carleton University Computer Systems Engineering September 2017 - December 2021 Graduated with distinction
Researcher Carleton University October 2023 - Present
Leading the design and testing of a real-time indoor 3D mapping system using a consumer drone (DJI Mini 3), integrating monocular camera and IMU with cloud-based SLAM framework for AR applications. Implemented modular software architecture using Docker, RabbitMQ, and React.js to offload high-computation tasks to remote server, enabling real-time visualization on desktop and AR headsets.
DevOps Engineer Magnet Forensics April 2022 - June 2023
Worked in a small team environment using DevOps tools including Jenkins, Linux, Python and PowerShell to help manage thousands of software builds a day on dozens of on-premise servers. Helped with the migration from on-premise to AWS cloud using CloudFormation and EC2.
Spectrum Engineering Co-Op Telesat May 2020 - December 2021
Made an alternative user interface and API in Python to interact with MATLAB giving the ability to directly use satellite XML data without requiring expensive MATLAB add-ons. Wrote extensive tests and code to analyze satellite spectrum use for international telecommunications regulations.
Programming Languages: Python, C/C++, JavaScript/TypeScript, MATLAB, Swift
AI & Computer Vision: TensorFlow, PyTorch, OpenCV, SLAM, Neural Networks, Computer Vision
Cloud & DevOps: AWS (EC2, S3, Lambda, CloudFormation), Docker, Jenkins, Linux, Git
Web Development: React.js, Node.js, HTML/CSS, REST APIs
Databases & Backend: PostgreSQL, SQL, Redis, FastAPI
Hardware & Embedded: PCB Design, Arduino, Raspberry Pi, Sensors, IoT
- AWS Certified Security - Specialty
- AWS Certified Solutions Architect - Associate
- AWS Certified Developer - Associate
- AWS Certified SysOps Administrator - Associate
- AWS Certified Cloud Practitioner
This portfolio includes a Model Context Protocol (MCP) server written in TypeScript that provides AI assistants like Claude Desktop with structured access to portfolio data.
What it does:
- Exposes portfolio resources (profile, projects, experience, skills, education, certifications)
- Provides tools to search projects, filter experience, and query skills
- Fetches live data from the deployed website
- Uses Zod for runtime schema validation
Architecture:
mcp-core.ts- Shared logic (tools, resources, data fetching)mcp-server-stdio.ts- Stdio transport for Claude Desktop (local)netlify/functions/mcp-server-http.ts- HTTP transport for remote access (in progress)
Setup for Claude Desktop:
-
Build the TypeScript:
npm run build -
Add to
~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"tom-sloan-portfolio": {
"command": "node",
"args": ["/absolute/path/to/Portfolio-Website/dist/mcp-server-stdio.js"]
}
}
}- Restart Claude Desktop
After setup, you can ask questions like:
- "What projects has Tom worked on with Python?"
- "Show me Tom's AWS experience"
- "What are Tom's AI-related skills?"
- Portfolio: tom-sloan.com
- LinkedIn: linkedin.com/in/tom-sloan
- GitHub: github.com/Tom-Sloan