01Overview
Job Summary
The hardest problem in our system isnt retrieval. Its deciding when two mentions are the same person.
A full name in one document. An abbreviation in another. A transliteration in a third script. An honorific. A description with no name at all. Everything above this depends on getting it right. Getting it wrong produces something worse than an error: a confident, well-sourced picture of a person who doesnt exist.
Why this work
India has been caught by surprise before. Not because the warning didnt exist, but because it existed somewhere in the system and nobody put it together in time. People died because of that gap. Were closing it.
Were building the intelligence system that helps India see the next attack coming before it happens, and helps it win the next war before the first shot is fired. Not with better sensors; India already collects enough. With the ability to actually use what it collects, at the speed the threat moves.
This doesnt get built by a foreign company, and it doesnt get built for a demo. It gets built by people who decided this mattered enough to build it here, for real, before its needed. If we do this right, the payoff is a warning that gets acted on in time, and a war thats already won in preparation before its fought at all.
What youd work on
Why its hard
At this scale all-pairs comparison is out, and standard candidate generation assumes comparable surface forms, the exact assumption that breaks first here. Rare-event base rates are punishing, which makes overall accuracy meaningless and puts all the weight on the top of a ranked list.
Who this is for
Given an ambiguous problem, you decompose it, name the tradeoffs, and design, rather than looking for a tutorial. You understand embeddings and retrieval mechanically. Youve worked on real, messy data, and you know a system thats right 95% of the time can be useless if the other 5% is catastrophic.
Direct experience with entity resolution, knowledge graphs, or multilingual .
