Open to Opportunities

Harshit Sharma

AI Product Manager

Product manager building at the frontier. I ship real products, work across the AI stack, and think obsessively about evals, reliability, and what makes AI systems trustworthy.

Selected Work, 3 Projects

Explainable AI Coding Assistant

Trust Through Transparency

Case StudyDeveloper Tools

The Problem

Developers waste hours verifying AI suggestions because tools optimize for speed, not trust, creating a “trust tax” that blocks adoption.

The Solution

Calibrated confidence scores + explainable reasoning. Show developers WHY a suggestion works and WHEN to be skeptical.

Market researchFigma prototypePDF case study
View Project

PM Salary Ace

Practice Like the Job Depends On It

Live ProductAI Tools

The Problem

PM candidates don't know what skill level they're actually at. Generic prep doesn't map to real compensation gaps.

The Solution

336 questions across 5 salary tiers. Practice what a $350K Staff+ PM actually needs, not generic frameworks.

22 users12 hours49% activation rate
View Project

Dear Her

Some feelings are too big for a text message

Live ProductAI Tools

The Problem

Most people feel things deeply about the women in their lives but never find the words to say it. The gap is not feeling. It is articulation.

The Solution

Write three honest prompts about her. Claude transforms them into a beautiful animated letter she can open from any link, no login required.

255 visitors48 letters10 countrieszero paid distribution
View Project

Background

Building AI Products That Scale Reliably

I started in electrical engineering, designing power systems where failure meant explosions, not just bad user reviews. That taught me to think through failure modes obsessively because in safety-critical systems, “it works in the demo” isn’t good enough.

For five years I shipped products at scale: complex enterprise software, data platforms, and revenue tooling that supported a $150M+ sales pipeline. I learned how to work with large cross-functional teams, drive adoption across organizations, and own outcomes end to end.

At Berkeley (M. Eng.), I’ve sharpened both edges: technical depth in ML, deep learning, and RL, combined with hands-on AI evals productization. I build products daily, not just study them.

I’m a daily AI power user: I code with Claude, build agents for automation, and live in the products I want to help build.

My focus: bridging the gap between AI capability and reliable deployment at scale because the companies that win won’t just have the most capable models, they’ll have the most trustworthy ones.

WHERE I STARTED

Non-traditional: safety-critical systems to enterprise SaaS. I’ve worked where failure = lawsuits and casualties, not just bugs.

WHERE I AM

Berkeley MEng. Deep in ML/RL/AI evals. Daily power user across frontier models. Building real products, shipping them, iterating.

WHERE I’M GOING

Building on the path to AGI. Where models are powerful but reliability is everything. Hard problems, high stakes.

Connect

Let's Connect!

Interested in AI product strategy? Building something cool? Just want to chat about agents and reliability? I'm easy to reach.

Currently: Trying to resurrect my chess game, playing too much table tennis, and planning my next trip to Japan (vegetarian ramen research in progress!).

If it's easier, pick a time directly

15 / 30 min · Google Meet