Senior Staff Technical Program Manager, AI Infrastructure at LinkedIn. 15 years across machine learning infrastructure, distributed systems, and reliability engineering.
Highlights
I recently sat down with Business Standard for their Manager's Mantra series (BSmart). What started as a conversation about automation and managerial roles turned into a wide-ranging discussion: enterprise cloud strategy, the economics of AI infrastructure, how to build data platforms people actually use, modernizing legacy systems,
Highlights
I presented our paper "Operationalizing Site Reliability in Large-Scale Distributed Systems: Shifting Ownership Left" at IEEE CSNT 2026 (Al-Khobar, Saudi Arabia): predictive models that forecast failures to keep large-scale distributed systems reliable.
Highlights
OneIndia profiled my work on AI model deployment: cutting deploy times from days to hours, lifting success rates from roughly 60% to nearly 99%, and serving about 4x more requests per GPU.
Highlights
I joined a Zee Business India 360° panel (Feb 2026) on AI hallucinations: why models confidently make things up, how to verify their output, practical prompt techniques, and where AI regulation stands across the EU, US, and India.
System Design
Every database we've met so far stores facts: a user's name, an order's total, a sensor's reading, a word in a document. The newest member of the family stores something stranger and more powerful: meaning itself. Vector databases are the data system
System Design
There's a kind of data that has quietly exploded in volume over the last decade, and you generate it constantly without thinking. Every time a server reports its CPU usage, every time an app records how long a request took, every time a thermostat notes the temperature, every
System Design
Type a few words into a search box and, in a fraction of a second, you get back the most relevant results out of millions or billions of documents, ranked best-first, with your typos forgiven and suggestions appearing as you type. We're so used to this that
System Design
Every database we've covered so far has one job: running an application. They store orders as they're placed, serve profiles as they're viewed, accept writes as they happen. This is the live, operational heartbeat of a product, and it's called OLTP, online
System Design
Every database family we've covered so far treats relationships as a secondary concern. Relational databases reconstruct them with joins at query time. Document databases mostly avoid them by nesting data. Key-value and wide-column stores barely acknowledge them. But for a whole class of important problems, the
System Design
After the elaborate machinery of relational, document, and wide-column databases, the key-value store is almost a relief. It is the simplest possible database, and that simplicity is exactly where its power comes from. There is no schema to design, no query language to learn, no joins to reason
System Design
Some data does not arrive politely. It pours. Think of every tap and swipe in a mobile app, every message in a chat platform, every reading from a fleet of sensors, every location ping from a fleet of vehicles, every line of a log from thousands of servers. This is
System Design
The relational database we covered in the last deep-dive is brilliant at one thing above all: taking your data apart into tidy, non-repeating tables. That is its great strength. But it is also, for some kinds of work, its great inconvenience. A lot of the time, the thing