AI Full Stack Developer · TS / Next.js / Node / AWS

Trading platforms.Investor-facing fintech.Shipped 0‑to‑1.

Equally at home in TradFi analytics and onchain systems. Two flagship SaaS — stox.my and TradeClaw — live in production. I think like a business owner: every system optimises for user delight, real-time correctness, and the unit economics of scale.

Remote · KL / MY · US-hours capable

Trading PlatformsInvestor-Facing FintechMulti-Factor ScreeningOnchain TokenisationAlpha DiscoveryMAS-Licensed Infrap50 80ms$50K → $200K0-to-1Trading PlatformsInvestor-Facing FintechMulti-Factor ScreeningOnchain TokenisationAlpha DiscoveryMAS-Licensed Infrap50 80ms$50K → $200K0-to-1

By the numbers

What four years at the intersection of finance and AI actually looks like.

What I do

Three things, deeply. Everything else is a side project.

01

Trading platforms & investor analytics

Stock analytics SaaS, multi-factor screeners, watchlists, AI-assisted alpha discovery. Real-time pricing pipelines, hand-written SQL, sub-100ms reads on hot queries.

  • Real-time market data ingestion + ranking
  • Momentum + fundamentals factor screening
  • Investor-facing dashboards tuned for data density

02

Community platforms & multi-agent bots

Community apps with points, leaderboards, and social loops — like the Gold Traders portal. Contest platforms that pull live standings from sources with no public API. And a different kind of chatbot: dynamic, multimodal agents that talk to each other while humans watch and step in.

  • Community apps — points, leaderboards, social automation
  • Contest engines with API-less data scraping
  • Multi-agent chat — bots converse, humans observe

03

End-to-end ownership

Spec, design, code, deploy, monitor, iterate. Smallest useful MVP, validate with real users, then scale. Highest-leverage work first. Founder posture — even inside a bank.

  • Full TypeScript across the surface
  • Python / FastAPI on AWS (ECS, Lambda, RDS)
  • Cloudflare-native edge: Workers, D1, KV, R2, Durable Objects

Selected work

Nine things I built that you can actually run.

Live production systems and open repositories. The full archive lives on GitHub.

Flagship · live SaaSCase study

stox.my

Stock analytics & alpha discovery SaaS for Malaysian markets. Real-time Bursa pricing, multi-factor screener, watchlists, AI alpha discovery. Edge-first, sub-100ms reads on hot queries.

Investor SaaSMulti-factorAI screeningEdge

Flagship · live SaaSCase study

TradeClaw

AI-powered trading signals SaaS across multiple asset classes including crypto. FastAPI + React, multi-tenant subscription product. Same shape as any modern signals platform — TradFi or crypto-native.

Signals SaaSFastAPIReactMulti-tenant
GoldTraders Trader Portal — frame 1GoldTraders Trader Portal — frame 2GoldTraders Trader Portal — frame 3

Production

GoldTraders Trader Portal

Built a portal (apps, AI signals, leaderboards, community automation) that lifted average monthly net ~$50K → $200K within 1 year. React 19 + Hono, 15+ REST endpoints, Durable Objects rate limiting, 4-mode contest engine with credit-integrity audit trail.

React 19Hono$50K → $200KAudit trail
HydraxRail — live product screenshot

Onchain · MAS-licensed

HydraxRail

White-label workspace for tokenised product issuance on MAS-licensed infra. Built on Canton Network smart contracts. Five role portals (Issuer, Distributor, Investor, Ops, Admin) with RBAC and audit-ready data model.

Smart contractsCantonTokenisationRBAC
SprintBo — performance story — live product screenshot

Cost-disciplined SaaS

SprintBo — performance story

Bot /ranking p50 1.4s → 80ms via D1 source-of-truth + KV cache shared across bot + Mini App + landing. Operator pause/close/cancel with tamper-evident final_snapshots. Hosting under $5/month.

p50 80msD1 + KVIdempotent cron
TradingHab Bot — live product screenshot

Signals SaaS · live

TradingHab Bot

Telegram analysis + signals across Crypto, Forex, Commodities, Indices, Stocks. On-demand chart breakdowns, Vision-LLM parsing, consistent schema across asset classes.

Multi-assetVision LLM5 markets
RoboForex Support Bot — frame 1RoboForex Support Bot — frame 2RoboForex Support Bot — frame 3RoboForex Support Bot — frame 4RoboForex Support Bot — frame 5RoboForex Support Bot — frame 6

Production AI agent

RoboForex Support Bot

Production AI agent — RAG with bge-m3 embeddings, query rewriting, multi-tenant routing, follow-up persistence. Python / FastAPI on AWS. Prompt + context engineering owned end-to-end.

RAGFastAPI on AWSbge-m3
continuous-improvement (npm) — live product screenshot

Open source · npm v3.9.2

continuous-improvement (npm)

Claude Code plugin + GitHub Action transcript linter. Skills, commands, hooks, agents, instinct packs, synthetic checks — structured self-improvement loops with eval-driven verification.

npmClaude CodeEval-driven

Live bot · type /help

Multi-Agent Chat

Not a typical support bot — a dynamic, multimodal chat where multiple AI agents converse with each other in real time while humans watch and step in. Each agent holds its own persona, memory, and modality.

Multi-agentMultimodalTelegram

In production right now

Captured from the live URLs this morning. No mockups, no slideware.

Every tile below is a Playwright screenshot of an actual deployed page that anyone can visit. Re-run npm run screenshots to refresh.

Career line

Investment desk, then engineering, now both at once.

  • 2024 — now

    Full Stack Engineer & AI Trading Systems · RM Investment Bank Ltd

    Built a trader portal (apps, AI signal tools, leaderboards, community automation) that lifted average monthly net ~$50K → $200K within 1 year — replaced what would have been a ~3-engineer + PM build. 15+ production apps across investor dashboards, signal bots, RAG agents, weekly contest engines.

  • 2023 — 2024

    Founder / Technical Lead · Tibyan AI Sdn Bhd

    Founded an AI-first venture. Secured seed investment from Cradle and Nexea. Owned product, engineering, and GTM end-to-end across Python, TypeScript, React, PostgreSQL, AWS — first-engineer posture from day one.

  • 2021 — 2023

    Manager — Growth & Operations · Salmi Niaga Solution

    Drove ~400% increase in marginal profit through better systems, automation, and execution discipline. Built Python analytics dashboards and reframed P&L from gross-revenue chasing to unit economics.

  • 2020 — 2021

    Investment Analyst & Data Engineer · Inter-Pacific Asset Management

    Contributed to AUM growth from ~$10M to ~$200M in ~1.5 years through data-driven research and screening. Built data pipelines, factor models, and analytics dashboards in Python and SQL.

    Best Shariah Equity Malaysia 2020 — Lipper / Refinitiv

  • 2016 — 2020

    Finance Executive · AmanahRaya Trustees Berhad

    Fund accounting, treasury operations, financial modelling. Excel/VBA automation that compressed reporting cycles — first hands-on exposure to the data shape behind investor analytics.

Stack

The tools I actually use to ship — not a fashion show.

TypeScript & Node (primary, ~5y)

  • Node.js
  • React 19
  • Next.js
  • Vite
  • Hono
  • Express
  • REST APIs
  • Hand-written SQL

React Frontend

  • React 19 hooks
  • Suspense
  • Tailwind
  • Data-dense dashboards

Databases

  • PostgreSQL
  • MySQL
  • SQLite / D1
  • MongoDB
  • Redis-style KV
  • Numbered .sql migrations

AWS & Containers

  • EC2
  • ECS
  • Lambda
  • S3
  • RDS
  • IAM
  • Docker
  • GitHub Actions
  • Terraform

Crypto / Onchain

  • Smart contracts (Canton)
  • keccak256
  • EVM signing
  • L1 / L2 patterns
  • Exchange APIs

Python & Backend

  • FastAPI
  • Flask
  • Django
  • Pandas
  • scikit-learn
  • Factor models
  • ETL

Edge & Distributed

  • Cloudflare Workers
  • D1
  • KV
  • R2
  • Durable Objects
  • Browser Rendering
  • Cron Triggers
  • Apache Kafka

AI & LLM

  • Anthropic Claude
  • Agent SDK
  • MCP
  • OpenAI
  • HuggingFace
  • RAG
  • bge-m3 embeddings
  • Prompt + context engineering

Contact

Have a problem at the intersection of finance, AI, and infra?

I read every message and reply within two working days. If it is not a fit, I will say so directly and probably point you to someone who is.

For agency / NDA briefs, send a one-paragraph summary first.