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 500K users as head of technology, ship production AI systems solo, and reported a valid finding in a $63K Code4rena audit.
AbstractAkash Bansal is a founding engineer who builds full-stack, AI and on-chain systems for technical teams, from architecture and backend to frontend, AI pipelines and cloud infrastructure as code. As head of technology he scaled a consumer platform to 500K users, and he built NextMantra, a production AI hiring platform, solo. He takes paid builds worldwide.
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 online 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.
Answer grading
Pin-compatible with your stack.
I drop into the codebase you already run — no rewrite into my favourite framework. Every pad below comes from real project work, not a tutorial checklist.
B1Languages
- TypeScript
- JavaScript
- Python
- Java
- C#
- Solidity
- Dart
- SQL
B2Backend
- Node.js
- NestJS
- .NET
- Spring Boot
- FastAPI
- REST APIs
B3Frontend & mobile
- React
- Next.js
- Tailwind CSS
- Flutter
B4Data
- PostgreSQL
- Prisma
- Redis
- Firebase
- BigQuery
B5AI & LLMs
- LLM pipelines
- AI agents
- Voice AI
- OpenAI
- Claude
- Gemini
B6Cloud & DevOps
- AWS
- GCP
- Azure
- Docker
- Kubernetes
- Terraform
- CI/CD
B7Blockchain & security
- Foundry
- Hardhat
- Yul · Huff
- Move · Sui
- DeFi · ERC-4626
- Smart-contract audits
- Formal verification
- Fuzz & invariant testing
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.
Three ways to wire me in.
I drop into the codebase you already run, or start from an empty repo, and own a vertical end-to-end. Work is async with overlap into US and EU hours, every build starts from a written design doc you can review, and you own the repo, the cloud accounts and the infrastructure code from day one.
MVP build
Architecture, backend, frontend and cloud infra as code, shipped to production in your repo and your cloud account. You get a running product and a codebase your next hire can read.
AI feature / AI agent build
An LLM pipeline, voice agent or AI workflow wired into the stack you already run — the same multi-LLM and real-time voice patterns behind NextMantra, scoped in a design doc before a line ships.
Fractional CTO / founding engineer
Architecture calls, written design docs, code review and hands-on shipping for a product area you hand over whole — the job I did as head of technology at Urban Culture, scoped to part-time.
Prices in USD are starting points. Final scope and price are agreed in writing before work starts.