Rubert Martín Pardo
Ml Platform Engineer @SymphonyAI
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WORK HISTORY
Ml Platform Engineer @SymphonyAI
Montreal, QC, CA
I build AI systems that enterprise clients actually use in production-What I do- Development of an orchestration platform that runs multiple AI workflows simultaneously. Some parts follow strict rules (conditionals, data transformations, email triggers), while others make intelligent decisions using AI and external tools. Basically: infrastructure that lets AI agents do real work reliably at scale- Created a customer-facing semantic search system that understands what users are really asking for, not just keyword matching. It searches through their company knowledge bases and gives contextual answers using large language models. Plain English questions and answers- Embed our workflows into other platforms for retail and finance teams. Lots of conversations that start with \"can we do?\" and end with working integrations that solve actual business problems- Constantly debugging, monitoring and talking to users — because AI in production breaks in creative ways and I need to actually listen to people to fix it properly.
EDUCATION
Instituto Balseiro
Bachelor's degree, Nuclear Engineering
McGill University
Doctor of Philosophy - PhD, Engineering
Instituto Balseiro
Master's degree, Nuclear Engineering
ABOUT RUBERT MARTÍN PARDO
I turn machine learning experiments into systems that scale - and I care about how the code is written. portfolio: https://rgmartin.github.io/These days, I work across the full lifecycle: training models, deploying them so they handle real traffic, and monitoring them when they inevitably do something unexpected. I\'m drawn to problems where standard solutions don\'t exist yet — the kind where you need to understand both the business constraints and the technical tradeoffs. I believe in software craftsmanship: writing code that others can read, extend, and maintain long after I\'m gone. That means tests that actually catch problems, clean abstractions that make sense six months later, and automation that prevents mistakes. I also believe in sharing what I learn — mentoring teammates and spreading knowledge is as important as shipping features. My background is unusual: I started as a nuclear engineer and spent years in fluid dynamics research. During my PhD, I got tired of expensive lab experiments and learned ML to solve the same problems computationally. A basic Support Vector Machine worked better than I expected, and I never looked back.
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