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Backend Engineer building scalable APIs, distributed systems, and AI-powered services with Node.js, NestJS, TypeScript, and Python — shipped end-to-end, from data modeling to production.
class BackendEngineer {
name = 'Sajib'
role = 'Backend Engineer'
stack = ['Node.js', 'NestJS', 'FastAPI']
languages = ['TypeScript', 'Python']
databases = ['PostgreSQL', 'MongoDB', 'Redis']
architecture = 'Microservices'
ai = ['RAG', 'Tool Calling']
deploy = 'Docker · AWS · CI/CD'
}
I’m a backend engineer who builds scalable, distributed systems and AI-powered services. My work spans the full lifecycle — data modeling, API contracts, business logic, deployment, and runtime — not just one layer of the stack. Coming from full-stack work, I design APIs around real UI needs: pagination, loading states, and error handling that the frontend can actually build on.
For systems that need to grow, I design microservices with clear boundaries and APIs with strong contracts. Whether it’s a modular monolith or a distributed system, my focus is keeping complexity under control as products and teams scale — not adding services for the sake of it.
I build features that need live behavior or intelligence — real-time logistics, RAG chatbots, tool calling, document retrieval. The goal is AI and real-time that solve real product problems, not bolted-on hype. I’ve shipped Uber-style tracking, payment engines, and AI automation that real users depend on.
I deploy what I build — Docker on AWS, CI/CD pipelines, Linux VPS behind Nginx or Caddy. I optimize for stability, observability, and systems that are easy to debug at 2am, not just impressive in a demo. Production is the deliverable.
I focus on backend systems and getting them to production reliably. Here's a snapshot of the stack I use most — languages, frameworks, data layers, AI tooling, and delivery.
TypeScript-first for type-safe backends; Python for AI, automation, and FastAPI services.
Node.js/NestJS for real-time and modular services; FastAPI for ML and async workloads.
Polyglot persistence — relational, document, and in-memory stores chosen per workload.
Typed data layers with Prisma and Drizzle; Mongoose for document models.
Containerized deployments on AWS, automated through CI/CD and reverse-proxied on Linux.
Live updates, signaling, and queue-driven background processing for product-scale features.
Production AI features — RAG pipelines, tool calling, and vector search over self-hosted or OpenAI models.
My professional progression through backend engineering, system architecture, and technical leadership.
A collection of my recent backend tools, infrastructure templates, and full-stack experiments. Need something built or deployed? I'm active on GitHub and open source communities. See Contact.
Email works great for a direct line. Prefer a structured collaboration? I'm active on open source communities and platforms like GitHub. I'm always open to a thoughtful message either way.
Send me a message for collaborations, inquiries, or just a friendly chat. I typically respond within 24 hours.
Insights on backend architecture, API design, and modern web development.