Founding Engineer Full-stack AI systems On-chain security ~6y in prod
I build what usually takes a team.
I own the entire arc — architecture, backend, frontend, AI pipelines, cloud infrastructure as code, and on-chain security. I scaled a consumer platform to 300K users as head of technology, ship production AI systems solo, and reported a valid finding in a $63K Code4rena audit.
From the silicon up to the top-level bus — one person accountable for every metal layer. No hand-offs, no seams, no "that's another team's problem."
NextMantra
An AI recruiter that runs the entire first round — live voice interviews, résumé-to-role scoring, and ranked shortlists — so hiring teams only ever meet the candidates worth meeting.
One engineer, the whole platform: real-time voice interviews on a low-latency speech pipeline, a multi-LLM scoring engine that grades each candidate against the role's own rubric, and the recruiter dashboards that turn it all into a defensible shortlist.
The deploy pipeline is trustless by construction: code ships AES-encrypted and tamper-evident, so a partner who alters what they shouldn't simply breaks their own service security that holds by structure, not by contract.
Urban Culture
Built and ran the entire technology for an at-home salon marketplace — four apps, custom infrastructure, and the systems that made the unit economics actually work.
A four-app ecosystem — customer app, partner app, admin console, and web — on a Flutter + Node + Firebase stack I architected and operated for four and a half years across six cities.
When Google's Distance-Matrix and geocoding bills threatened the margins, I replaced them with a custom geolocation service −80% cost — accuracy held.
Then the systems that moved the numbers: a targeted coupon engine +16% cart, a partner loan-management system +30% onboarding, and a Firestore → BigQuery pipeline feeding every decision.
KAF·1
Turns chaotic, spreadsheet-and-email customer onboarding into a repeatable, SLA-tracked pipeline — so customer-success teams ship predictable go-lives instead of chasing threads. Create a company, assign a workflow, assign an agent; managers watch bottlenecks before they become delays.
Customer success
BNB·2
A yoga & meditation studio taken end-to-end — public site, class / workshop / retreat booking, and ₹ payments — fronted by a free wellness-assessment quiz that converts cold visitors into booked sessions and first-party data the studio actually owns.
End-to-end
TE·3
An AI precision testing engine for competitive-exam prep: it diagnoses exactly what's holding each learner back, maps the gap against their target-exam blueprint, and drills precisely that — to move the one number that matters, passing probability. Sold direct to aspirants and to the coaching institutes that train them.
Building now
AV·4
An always-on sourcing analyst: it reads inbound résumés straight from Gmail, scores them, and surfaces the strongest — unattended. Engineered around Firestore's hard query limits with cursor pagination and a bounded concurrency pool so it never trips a quota.
Automation
SEC·5
Adversarial security work: a valid finding in a $63K Code4rena audit, formal verification with Halmos & Certora, low-level EVM (Huff / Yul), and Sui / Move — the same threat-model-everything discipline I bring to every system I build.
Adversarial
AI·6
Grades long-form exam answers at scale: an event-driven, multi-LLM pipeline — Gemini OCR → semantic matching → Claude scoring on a weighted rubric — with human-expert override where it counts.
Mains evaluation
I don't accept "should work." I trace every path to bedrock.
Trace to bedrock
Decompose a problem to its smallest provable truth, then build up. Fix the root cause — never patch the symptom.
Fail safe, not silent
Design so that when an assumption breaks, the system stops — it doesn't quietly lie. Bad behaviour should be self-defeating.
Structure over trust
Trust isn't a security feature. Guarantees come from how a system is built, not from how carefully it's operated.