Full-Stack Software Engineer (AI-Native)

KEY ACCOUNTABILITIES

Product engineering

• Build the worker-facing mobile experience and the employer-facing web console: job posting, criteria setting, booking, QR-based clock-in and clock-out, two-way ratings and worker profiles.

• Build the backend services behind them: shift lifecycle, matching and broadcast, notifications, scoring, and preference lists.

• Own features end to end — specification questions, implementation, release, monitoring and the follow-up fix.

Matching and trust systems

• Implement the criteria-based matching engine, together with the transparency and appeal path expected of automated decisions under local regulatory requirements.

• Implement graduated trust levels, cancellation handling and reliability scoring, designed so that a single poor first shift does not end a worker's access to the platform.

Quality and velocity

• Maintain test coverage, CI discipline and review standards proportionate to the risk of the code in question.

• Use AI coding agents and platforms aggressively for scaffolding, refactoring, test generation and code comprehension — and review every line before it merges.

• Keep AI tooling spend inside the agreed per-seat cap.

Cross-functional collaboration

• Work directly with the Product Owner and Product Designers on a weekly build cycle; take part in discovery sessions and user testing rather than receiving their conclusions second-hand.

• Instrument every feature so that Product, Marketing and Operations can read the numbers without asking engineering.

MINIMUM ESSENTIAL QUALIFICATIONS

Education

• Bachelor's degree in Computer Science, Software Engineering or a related discipline — or bootcamp or self-taught background supported by a portfolio of shipped production work.

Work Experience

• 2 to 5 years of professional full-stack development, including at least one product shipped to real users and maintained in production afterwards.

• Demonstrated daily use of AI coding agents and platforms (Claude Cowork or Claude Code, Cursor, Lovable, Copilot or equivalent) in real delivery work — able to show what was built and how the workflow genuinely differs.

• Comfortable operating without specialists: has debugged their own infrastructure, written their own tests and spoken to their own users.

• Startup or small-team experience preferred; marketplace, on-demand, fintech or HR-tech domain experience an advantage.

TECHNICAL COMPETENCIES

Frontend and mobile

• React with TypeScript to a strong professional standard; hooks, state management, component architecture and performance profiling.

• Next.js (App Router, server components, server actions) for the employer-facing web console.

• React Native with Expo for the worker mobile app; navigation, push notifications, deep linking and over-the-air updates.

• Tailwind CSS and a component library discipline; responsive and offline-tolerant behaviour.

• Camera, QR scanning and geolocation APIs on mobile, and performance engineering for low-end Android devices — the majority of the worker base.

Backend and data

• Node.js and TypeScript; REST and typed API design, with tRPC or GraphQL an advantage.

• Supabase in production: PostgreSQL schema design, Row-Level Security policies, Auth, Realtime subscriptions, Storage and Edge Functions (Deno).

• SQL to a working standard: query tuning, indexing, migrations and data integrity constraints.

• Background jobs, queues and scheduled work; real-time push and notification delivery at volume (FCM, APNs).

Platform and delivery

• Git-based workflow, pull-request review, CI/CD (GitHub Actions or equivalent) and trunk-based delivery.

• Deployment on Vercel, Supabase and equivalent managed cloud services; containerisation basics.

• Observability: logs, traces and dashboards — enough to diagnose your own production problems.

AI-assisted engineering

• Prompt and context engineering for coding agents; decomposing work into agent-suitable units.

• Critical review of generated code; awareness of token cost and how to control it.

• Familiarity with rapid-prototyping platforms such as Lovable, and sound judgment on when generated scaffolding must be rewritten before it reaches production.

Product and communication

• Reads a metric, forms a hypothesis, and can argue for or against a feature on evidence.

• Working English; Bahasa Malaysia an advantage for user-facing copy and worker testing.

TOOLS & WORKING ENVIRONMENT

• AI development: Claude Cowork and Claude Code, Cursor, Lovable — used daily, with per-seat budgets and review discipline.

• Application stack: TypeScript, React, Next.js, React Native (Expo), Node.js, Tailwind CSS.

• Data and backend: Supabase (PostgreSQL, Auth, RLS, Realtime, Storage, Edge Functions).

• Delivery: Jira, Git and GitHub, GitHub Actions CI/CD, Vercel, Figma for design handoff.