About
AI Systems Architect & Full-Stack Engineer with 16+ years building production systems across AI, IoT, enterprise ERP, and scalable cloud platforms.
I specialise in turning complex technical challenges into working systems — from LLM-powered RAG pipelines and digital avatar kiosks to industrial IoT networks and billing infrastructure. Strong hands-on engineer with architectural leadership experience, focused on systems that are reliable, maintainable, and built to scale.
How I Think
Architecture principles I apply on every system — these separate systems that last from ones that don't.
Modular over Monolithic
Prefer modular monoliths or bounded microservices — clean module boundaries first, split when scale demands it.
Queue-Based Async by Default
Decouple heavy operations with BullMQ and Redis queues. No blocking the critical path with side effects.
Offline-First for IoT
Edge devices must function independently. Design for reconnect, not constant connectivity.
Right DB for the Right Job
PostgreSQL for relational integrity, MongoDB for flexible schemas, Redis for caching, InfluxDB for time-series.
AI as a System Layer
LLMs aren't features — they're infrastructure. Design RAG pipelines and agents as first-class system components.
Scalability Without Rewrites
Architecture decisions today shouldn't become bottlenecks tomorrow. Design for the next 10x, not just the current load.
Professional Philosophy
Systems Thinking
Every component exists within a larger system. I design with the full lifecycle in mind — from local dev to production scale, edge devices to cloud backends.
Business Outcomes First
Technology is a means to an end. Every architectural decision is evaluated against the real-world impact it delivers — efficiency, cost, reliability, or speed.
Industry Experience
Architected and delivered solutions across complex domains, adapting engineering principles to industry-specific challenges.