Capabilities, architecture, and evidence.
How I contribute to an engineering team or product — a practical map of backend development, data, infrastructure, delivery and architecture.
What I do
Backend Development
Production-ready backend systems using Laravel/PHP, Node.js and TypeScript — APIs, business logic, authentication, integrations and database architecture.
API Design
Structured REST APIs with clear contracts, validation, error handling, pagination and maintainable service boundaries.
System Architecture
Turning requirements into maintainable architectures, modular systems, service boundaries and deployment strategies.
DevOps & Deployment
Containerizing applications, building deployment workflows, configuring Linux environments and improving delivery reliability.
CI/CD
Automated pipelines for testing, building, containerizing and deploying software.
Technical Problem Solving
Investigating difficult application, deployment, infrastructure and integration problems and turning them into practical solutions.
AI in my practice
AI-Assisted Development
AI coding agents, GitHub Copilot, ChatGPT and Claude are part of my daily workflow — writing, debugging, testing, reviewing and documenting production software. AI is a tool in my process, not a separate specialty.
AI Application Engineering
I have shipped a production AI/LLM integration. The layer AI features run on — backend APIs, authentication, data modeling, Redis caching, queues and async jobs — is my core discipline.
Backend & AI Infrastructure
Docker, Kubernetes, cloud server management and DevOps delivery transfer directly to AI workloads: containerizing AI services, deploying and scaling them, and keeping them observable.
AI Growth Areas
LLM application architecture, RAG systems, AI agents, vector search, MCP servers, AI observability and evaluation — prioritized to extend my backend and infrastructure base.
Where I fit in an AI-driven engineering team
My value isn't simply knowing how to call an LLM API. It's the production backend, data, infrastructure and deployment that make AI features real — plus the ability to integrate the AI layer itself.
Each layer below is a capability I bring from shipping production systems. The AI application layer is where I'm actively building.
Where I'm going
How production systems are put together
The pattern I build toward: an application layer over clear service boundaries, backed by durable data and wrapped in containerized, observable delivery.
Detailed engineering work
Integrated Digital Service Delivery Platform
Microservice-based digital service delivery platform for the Ministry of Chittagong Hill Tracts Affairs.
Online Exam & Certificate Management System
Online exam and certificate management system for the Department of Shipping, built with PHP, Laravel and Next.js.
DLS Automation System
Microservice automation platform for the Department of Livestock Services, built with PHP, Laravel, Vue, Nuxt and Node.js.