I'm a Computer Engineering student (UAlberta, Software Option Co-op, graduating June 2027) who
builds end-to-end systems: the machine learning that makes decisions, and the full-stack platforms
that put those decisions in front of the humans who act on them.
This year at BossPac Engineering & Technology, I designed and deployed a production multivariate
anomaly detection pipeline for broken-rail prediction — the leading cause of mainline freight
derailments. The pipeline produces a second-opinion vote on threshold-based alarms
and has cut false-positive alerts by ~52% in live operation across Class 1 freight subdivisions in
North America. I own the MLOps layer — rebaselining, drift detection, per-subdivision
scalers, and precision/recall evaluation against confirmed outcomes.
I also built the full-stack platform that operationalizes it. BRIM runs on the PERN stack
(PostgreSQL, Express, React, Node.js) deployed on AWS with Redis session management, Cypress and
Jest test suites, and AWS Cloudscape components — delivering real-time geospatial alerts,
configurable condition-based dashboards, and field-deployment tooling to 10–50 rail operators
across 7,650+ miles of Class 1 freight track. The ML is only useful because the platform makes it
legible to the humans making safety calls.
Before BossPac, I worked in Process Automation & Controls at Suncor Energy's Base Plant, a ~350
kbpd Alberta oil sands operation, where I developed an input signal conditioning algorithm for a
hydrogen flare controller that enabled Advanced Process Control engineers to commission a Model
Predictive Control strategy now delivering ~$13K/month in sustained operational savings. I also
built a site-wide PLC diagnostic web app adopted by ~10 daily engineers and technicians, cutting
fault diagnosis time from 30+ minutes to under 10. On the side, I red-team frontier multimodal AI
models as an RLHF contractor at Outlier AI, focused on reasoning-chain failures, hallucination
patterns, and tool-use errors across 300+ technical tasks.
I'm looking for teams where both sides of the stack matter — where the model has to work in
the field and the interface has to work for the user. Industrial AI, autonomous systems,
robotics, safety-critical infrastructure, and applied AI at companies that care about the gap
between a model in a notebook and a model in production. I'm equally happy owning general
full-stack or backend work at companies building things that matter; the common thread is
real-world systems where correctness, reliability, and human trust are non-negotiable.