Engineer turned GTM lead at AWS, Google, and most recently Meta. Former D1 athlete. AI builder on the side.
I studied Information Systems at George Mason University, where I ran Division 1 track & field.
I've spent my career at the crossroads of engineering and go-to-market, with a front-row seat to some of the biggest AI launches of this era.
I enjoy building and learning about AI tools that power everyday workflows, like CS Churn Radar and the YouTube Health Partner Scorer, plus personal projects like The Daily Grind, NFL Girly Pop, and TasteMatch. Check out my AI projects ↓
From the track to AWS, Google, and Meta. The race log is drawn to scale. Tap any split to open the full resume entry.
Over five years, I've worked where cloud engineering meets go-to-market. I started at AWS, building my cloud engineering foundation as the technical contact for large media accounts like ESPN and Disney. At Google Cloud, I became a Customer Engineer (Google's sales engineering role), building technical demos and helping enterprises modernize their tech stacks.
As Google answered ChatGPT with Gemini, my role shifted to scaled customer success, where I helped design and scale a program for 300+ VC-backed startups building and growing on Google Cloud during the AI boom. Most recently at Meta, I was the primary technical partner for 50 media partners, building the automation and dashboards that kept their integrations healthy.
George Mason UniversityB.S. Information Systems · May 2021
D1 Track & Field · A10 Champion · Commissioner's Honor Roll
AWS Cloud PractitionerGCP Cloud Digital Leader
GCP Professional Cloud Architect (in progress)
Tools I'm building to help teams learn faster, act sooner, and walk into every conversation prepared. Some are open source for anyone to build on, and some are just for fun.
A daily briefing built the way enablement should work: the right information, in five minutes, in the format people actually use. Every episode is curated and written by model inference, then narrated by a voice model I trained on my own voice with ElevenLabs, so you can read it or listen on the go.
Gives CSMs and AEs a clear read on their book in seconds: which accounts are at risk, why, and the play to run next. It runs as an MCP server, so reps can ask Claude or Google Antigravity about their accounts in plain English instead of digging through dashboards.
Under the hood, usage and adoption signals (55%) and contract and renewal signals (45%) roll up into a 0–100 churn-risk score. Each flag comes with a recommended retention play and a talk track, so newer reps know exactly what to do and say.
Closer to the center means higher churn risk. Dot size shows ARR. Tap a dot or a total.
$ git clone github.com/itspuneettt/cs-churn-radar ↵
Helps partnership teams decide where to spend their time. It scores potential health creators on growth, content alignment, and readiness, then pairs each one with a recommended play and a one-line takeaway for the partner manager. It turns the partner prioritization I did at Google and Meta into a repeatable playbook.
Higher dots are growing faster, dots further right fit the brand better, and dot size shows audience. Tap a dot or a tier.
$ git clone github.com/itspuneettt/youtube-health-partner-scorer ↵
Tech that makes the fun stuff better.
An NFL translator that turns football jargon into girly-pop speak, built so I can follow along with my husband on Sundays.
Before: nodding along. After: following every play. Try it ↗
A personal recommendation engine for books, films, TV, and music. Share your favorites or upload your Goodreads, Letterboxd, or Netflix history, and it suggests what to read, watch, or play next, with links to Spotify and Apple Music.
Before: endless scrolling for something new. After: picks that match your taste. Try it ↗
An open-source luxury bag comparison tool, built in TypeScript, that you can deploy as your own.
Before: ten tabs of bags. After: one side-by-side. GitHub ↗
From ESPN and Disney at AWS to sponsorships at Google and creator activations at Meta, sports have run through my whole career. Now I'm learning the business side.
I was selected as a mentee for the South Asians in Sports Mentorship Program, an 8-week immersive program pairing South Asian professionals with leaders across the sports industry. This fall's cohort is the largest in the program's seven-year history, with 56 mentees and 45 mentors.
I'm paired with a founder building digital fan activation for pro teams, and my focus is how partner activations come together and how AI is changing fan engagement.
See the cohort announcement ↗Read it as a newsletter or play it as a podcast. There's one daily edition, about five minutes long.
Written by model inference and narrated in my own AI-trained voice.
Five years at AWS, Google, and Meta, a D1 track background, and a habit of building with AI. If you're working in tech or sports, or have a fun demo or idea to swap, I'd love to connect.