About
- who
- Matthew McDowall — applied AI engineer.
- what
- Custom, domain-specific, end-to-end AI systems that ship to production. Not demos.
- where
- Currently Applied AI & Automation Engineer Lead @ North Light AI
- degree
- B.S. in Data Analytics & Data Science, University of New Hampshire, class of 2026.
Experience
Applied AI & Automation Engineer Lead
Intern (Dec 2024) → part-time engineer (Sep 2025) → full-time engineering lead (May 2026), owning the design and production rollout of North Light AI's core products and managing interns.
Prime Ready — NHADC
Agentic LLM matchmaking platform that pairs small-to-medium suppliers with Prime contractors through natural-language search, cutting 6+ weeks of manual discovery to minutes. Concept to production in 2 months with a 4-person team; now reaching 200+ SMBs and a NASA deployment. Tuning the hybrid retrieval stack raised hit rate@5 from 74% to 92%.
Lesson Desk — North Light AI
AI-powered teaching platform that automates lesson planning and content creation so educators get their time back. Leading development from architecture through production: rolled out to a small group of K-12 teachers, in partnership with the University of New Hampshire, with a rich in-browser editor and export to the formats teachers actually use.
AI-Driven NMS Chatbot
- Locally hosted assistant for SubCom's subsea cable Network Management System, fully air-gapped with zero external API calls
- Parses proprietary NMS documentation with Docling, embeds it into ChromaDB, and answers natural-language questions grounded in the source docs
- 94% retrieval accuracy (P@10, NDCG@5) at sub-second latency on VRAM-constrained VMs
Student Intern
- Built an LLM-powered co-advisor tool end to end under a Google-funded grant: a RAG pipeline over the advisor knowledge base returning grounded, context-aware answers, moving advisors from a 2:1 to a 1:1 advisor-to-client ratio
- Worked directly with SBDC advisors across the country to identify client-support best practices that shaped the tool's design and feature prioritization
Data Analyst Intern
- Engineered an automated data-processing pipeline for weekly coastal measurements using Python and scheduled cron jobs, cutting manual staff effort by ~10 hours/week
- Designed data-validation protocols with ArcGIS and Python that improved measurement accuracy by 25% and streamlined data access for researchers and external stakeholders
Education
B.S. in Data Analytics & Data Science
Relevant Coursework: Algorithms, Machine Learning, Linear Algebra, NLP, Reinforcement Learning, Information Retrieval, Probability & Statistics, Game Theory