I build data systems that hold at scale, and study how models learn to query them.
I am Diego Lopes — 8 years of production data engineering, currently leading a data platform, and an MSc researcher at IME-USP working on natural language interfaces for databases.
The engineering runs at the boundary of storage engines, high-throughput pipelines and microsecond latency. The research asks a harder question: can a model read a schema it has never seen and write a query that is actually correct?
Production Apache Cassandra deployment serving 1M+ connected devices at 13μs P50 write latency and 99.99% availability, at 75% lower cost than the managed alternative.
Every introduction to Cassandra says it is write optimised. What it rarely says is that this applies to DELETE too, and that the bill for those writes arrives on the read path, often as a timeout nobody can explain.
Term frequency tells you how often a word appears. Inverse document frequency tells you whether that means anything. Here is the maths, worked end to end on a corpus small enough to check by hand.
When I am not designing pipelines or fine-tuning models, I fly.
The overlap with engineering is larger than it looks. Both reward meticulous planning and punish improvisation. Both require making a decision with incomplete information, on a clock, and then living with it. Both are mostly about what you do when the plan stops working.
Aviation taught me to always have a second option ready before I need it — which turns out to be a reasonable description of designing for failure in a distributed system.
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Let's build what does not exist yet
I am working toward staff and principal engineering problems at the intersection of large-scale systems and language model research — the kind that do not fit neatly into one job description.
Ollie · 7 moPulsus · 3 yr 6 moACT Digital · 3 moCI&T · 1 yr
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