Revenue-critical frontend
Payments, checkout, and onboarding work where speed, reliability, and business outcomes have to move together.
Senior Software Engineer, Platforms & Applied AI
I build revenue-critical frontend and Applied AI systems teams can trust.
I'm Rakesh Cheekatimala, a Singapore-based engineer with 10+ years across payments, eKYC, property, commerce, and enterprise platforms.
- 60%
- bundle-size reduction on a payments app
- 25%
- sales lift from new payment methods
- 30%
- test automation coverage increase
- 10+
- years across Singapore engineering teams
What I'm hired to improve
Calm engineering judgment for frontend systems.
I focus on performance, clean architecture, reliable tests, and developer tooling that helps teams ship with confidence.
Platform architecture
Micro-frontends, shared UI standards, Storybook documentation, and integration boundaries that help teams scale cleanly.
Delivery confidence
Testing strategy, CI quality gates, Lighthouse workflows, Sentry visibility, and internal tooling that reduce delivery friction.
Building now
Production discipline, applied to AI tooling.
These projects make the Applied AI direction visible without pretending the last decade of frontend and platform work did not happen.
AnswerLint
An open-source CLI and web product that audits whether content is ready for answer engines, AI search, and citation-driven discovery.
Deterministic AEO/GEO checks, llms.txt generation, CI quality gates, diff reports, and evidence-backed recommendations.
TypeScriptCLIAI visibilityCI
SkillTrustOps
A focused experiment for reviewing AI agent skills before they are allowed to run inside an organization.
Connects AI safety, policy checks, and practical platform governance into a product-shaped prototype.
AI safetyAgent skillsGovernance
APISpec to MCP
A developer-tooling exploration for turning API specifications into MCP server implementation plans.
Shows the bridge between production API thinking, agents, and developer workflow automation.
MCPAPIsDeveloper tools
Engineering notes
How I think about platforms, agents, and trustworthy AI systems.
Proof of judgment
I make systems easier to ship, debug, and trust.
- Reduce frontend weight before optimizing surface details.
- Add observability where debugging is expensive.
- Use CI gates, tests, and audits to protect release confidence.
- Treat AI systems as production infrastructure, not demos.
- Document reusable patterns so teams move faster.
Selected work, with the outcome first.
Case Studies- 01View case study
Payments Performance Modernization
Reduced bundle size by 60% and supported faster checkout performance.
ReactTypeScriptPerformancePayments - 02View case study
eKYC Micro-Frontend Platform
Streamlined delivery across eKYC journeys while keeping teams aligned on shared frontend standards.
ReactRollupMicro-FrontendStorybook - 03View case study
Developer Tooling & Quality System
Improved developer experience through internal CLI automation, component documentation, testing standards, and CI quality gates.
Node.jsStorybookCypressJest