Engineering Lead
I turn ML research into systems that work at scale. Eight years in industry — and a Master's in Machine Learning currently in progress — taught me that the hardest part isn't the model. It's everything around it.
My background is a bit unusual: Computer Science meets Psychology and Neuroscience. It shapes how I think about building systems that work for people, not just on benchmarks.
A mix of enterprise delivery, technical leadership, and hands-on engineering.
Architected data integration across three internal sources for a retrieval and semantic-search platform serving 4,000+ global researchers. Adoption reached thousands of monthly queries within weeks.
Owned infrastructure for four research environments serving 4,000+ global users at 99%+ uptime, coordinating incident response across cloud, database, networking, performance, and vendor teams.
Founded and led a four-year machine-learning collaboration, mentoring 70+ students. The program produced hires and ML results adopted into business operations.
Built a voice-driven Android proof of concept integrating corrosion forecasting for hands-free field inspections, then presented it to executive and global teams to expand adoption.
Built 85+ regression and classification pipelines, doubled best-model precision, and reduced predicted failure probability to 13% through optimization.
Built an interactive PDF/CDF explorer that makes common probability distributions easier to inspect and compare.
How I work
I translate complex needs into technical direction, lead multidisciplinary teams through delivery, and measure whether what we build creates impact.
Clarify the need, users, constraints, and technical context.
Set KPIs and technical guardrails before implementation.
Fortune 200 Manufacturing Company — 7+ years
The Hidden Cost of AI Assistance: Outsourcing Critical Thinking.
Open to engineering leadership and technical roles across software, data, and AI. If you're building something interesting, let's talk.
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