proof of value used to be simple. you solved a business problem. shipped features. scaled systems. clear metrics. but ai scrambled everything. now founders look for weird signals. have you built an mcp server? touched agent architectures? worked with acps? they're checking if you're up to date, not if you're good.
for ai in india is still evolving. most are early stage. the ones that exist care more about freshness than experience. four year old ai work? irrelevant. two year old work? ancient. this makes showing proof of value nearly impossible. but there's good news.
every technology shift reshapes how companies organize. understanding how ai companies are setup tells you where opportunities live.
second, your fitment frontend or backend, your path differs. we'll decode the core business problems companies actually face.
from there, learning ai foundations and skills that matter. for fe vs be vs devops engineers.
finally, the meat: picking projects that demonstrate real value
it always reflects in the way a team or the org is set up to deploy the technology. let's go way back to the industrial revolution to then, let's say, a tata was set up back in the day for a steel company. to the next revolution around computers. the way an ibm or an apple hardware division was set up. and then the internet came. to deploy internet based products fast we got fe, be and devops.
you cannot take an internet based company's structure and go and deploy like, you know, hardware. that setup won't work. and now with ai that’s changing.
ai org structures are nuanced. i spent hours mapping job postings from openai, cursor, lovable, bolt, replit and more. this is my understanding after researching across hundreds of ai companies, looking at their job descriptions, whether it's foundational companies like openai, anthropic, deepmind. or it’s application layer ones like cursor, lovable, bolt, & replit. let’s dive in.
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infrastructure