About
I started with interfaces. Now I build the systems behind them.
I am Aviral Malik, a Full Stack Software Engineer with 4+ years of experience building scalable SaaS products across frontend architecture, backend service layers, and cloud-aware delivery.
My foundation is in React, TypeScript, and frontend architecture, but my work has expanded into backend services, API design, cloud architecture, database-backed workflows, automation, and AI-enabled product experiences.
I care about the complete product path: how the interface feels, how the data flows, how the backend responds, how the system scales, and how the experience remains reliable for real users.
My engineering evolution
UI Craft
React, Next.js, UX, accessibility, and polished interfaces.
Frontend Architecture
Micro-frontends, component libraries, design systems, testing.
Full-Stack Ownership
Node.js, NestJS, APIs, databases, and workflow logic.
Cloud and Scale
AWS, serverless, caching, deployment, and infrastructure thinking.
AI and Product Systems
Assistant workflows, automation, and intelligent UX.
Working principles
Design is part of engineering.
Architecture should reduce future pain.
Good APIs make good frontends possible.
Performance is a product feature.
Documentation and clarity multiply team speed.
Flashy UI is valuable only when it serves trust and usability.
Open to relocation to Japan with visa sponsorship. Prefer English-speaking software-engineering environments. Actively learning Japanese (targeting JLPT N3/N2 over time).
Skills by layer
Frontend
Polished, accessible, high-performance interfaces and data-intensive SaaS workflows.
Frontend Architecture
Micro-frontends, modular architecture, legacy refactoring, and zero-to-one product surfaces.
Backend and APIs
Service-layer implementation, REST APIs, and backend contributions across form and audit workflows.
Cloud and Data
Practical cloud and data skills used across SaaS and portfolio systems — not a dedicated cloud-architect title.
AI and LLM Work
LLM integration, prompt engineering, and prototype AI workflows — not model training or MLOps research.
Engineering Tools
Day-to-day delivery, testing, and observability tooling.