I build products that turn complexity into clarity.

Over five years, I have shipped products across AI, SaaS, and enterprise systems. Reducing document processing time by 70 percent, Improving fulfillment efficiency by 30 percent, driving 60 percent repeat order growth, acquiring 3.3+ million users and generating over $100 million in revenue.

But metrics alone do not define my work. What defines it is leverage.

From Engineering to Product

My career began in engineering - building ETL pipelines, optimizing data workflows, and debugging systems at scale. I learned how data moves, how architecture determines performance, and how systems fail long before a user ever sees an interface.

But I wanted to operate one layer higher. Not optimize isolated components, but shape the system itself.

That shift led me into product management, where the real work happens at the intersection of user behavior, business incentives, and technical feasibility. Engineering gave me the instinct to ask why something breaks. Product gave me the discipline to decide what to build instead.

Building at Scale

At Filo, I helped launch an instant tutoring platform in the US that scaled to over 120,000 users in six months. Through funnel redesign and rapid experimentation, we increased activation by 35 percent and grew signups 125 percent quarter over quarter.

At Closphere, I led the roadmap for a cloud-based inventory platform - improving time to ship by 30 percent and growing to over 1,000 organic users through tight feedback loops and workflow-first thinking.

At ProductSquads, I led end-to-end development of two AI-native tools: an API testing automation system that cut QA effort by 40 percent, and an intelligent document extraction system that accelerated processing by 70 percent.

Each role reinforced the same instinct: build systems that compound in value as they scale.

Looking Inside the Black Box

In early 2025, I started shipping real AI products powered by large language models. The magic was immediate. So was the risk.

Models hallucinate. Prompts drift. Latency drives up cost. Evaluation is messy. Trust is fragile and slow to build.

I did not want to be another PM wrapping someone else's model in a thin UI. I wanted to understand the mechanics underneath - how models reason, how to evaluate them honestly, how to design feedback loops, and how to build AI products that are defensible rather than disposable.

That pursuit brought me to the University of Washington's MS in Information Management program, specializing in Product and AI. I came to demystify the black box and sharpen my ability to productize AI responsibly at scale.

Today, I think about AI not as a feature, but as infrastructure.

What Comes Next

I am graduating in August 2026 and looking for Mid to Senior Product Manager roles where the problems are hard and the stakes are real.

I am drawn to teams building intelligent systems that amplify human judgment rather than replace it - products that move beyond experimentation into infrastructure, platforms that shape how millions of people work, decide, and create.

My background across India, Singapore, and the United States has given me a global lens on how context shapes adoption and how nuance determines trust. I have built for consumers, SMBs, and enterprise clients. I know that the same product can mean entirely different things depending on who is holding it.

The PMs who will define the next wave of AI are not the ones who ship fastest. They are the ones who build with clarity, hold the line on quality, and understand that trust, once broken, is the hardest thing to rebuild.

That is the kind of PM I am building toward. And I am almost there.