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Miftaul Islam Shuvro

Miftaul Islam Shuvro

DevJobs TeamAvailable

Full-Stack Engineer (Node.js, .NET, Go, React, Next.js) | System Design · Multi-Tenant SaaS · Serverless · GraphQL · LLM Integration | Cut API latency 30–40%

Australia
3 yrs experience

I'm a full-stack engineer with 3 years of experience. I work across Node.js, Go, React, and Next.js, currently at Web Makers building CREBrains, a Commercial Real Estate automation platform.

At Web Makers, I designed and shipped a multi-level RBAC system handling permissions across organization, transaction, task, and document scope. I built Node.js and GraphQL services on AWS Lambda, alongside Go services for latency-sensitive paths that cut response times 30–40%. I've optimized frontend performance—replacing naive list rendering with virtualized infinite scroll (4.2s → 1.1s load time)—and built a drag-and-drop form builder that reduced support tickets 80%. I also work with Python services for LLM-based document processing in production workflows.

My stack includes Node.js, Express, Fastify, NestJS, GraphQL, AWS Lambda for backend work; Go, AWS, Azure, and serverless architecture for infrastructure; React, Next.js, TypeScript, and Tailwind CSS on the frontend; and Angular, Tauri, and Sass when needed.

Skills

PythonGoTypeScriptReactNextjsAWSAzureAngularTauriTailwind CSS
Web Makers LTD

Full Stack Engineer

Current Position
Web Makers LTD

Jun 2025 – Present

  • Designed the multi-scope RBAC system for a Commercial Real Estate automation platform, enforcing permissions across 4 object levels (organization, transaction, task, document), by modelling the permission graph and access logic from scratch.
  • Cut API response times 30–40% on the platform's highest-traffic endpoints by extracting them from Node.js into dedicated Go services.
  • Shipped 8+ core product features from raw business requirements to production in 13 months while leading 2 developers, owning the modular backend architecture.
  • Built the platform's serverless backend on Node.js, GraphQL, and AWS Lambda with DynamoDB, keeping infrastructure cost flat as workload scaled.
  • Integrated LLM-powered document processing using Go and Python services, automating document classification and data extraction while reducing token usage and manual review.

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