I design backend platforms, distributed systems, data flows, APIs, and AI infrastructure. My standard for architecture is practical: it should make ownership clearer, failures smaller, operations calmer, and the next change less expensive.
An impressive diagram is not evidence of a good system. Running behaviour is.
How I approach architecture #
Begin with boundaries and invariants #
Service boundaries should follow ownership and change patterns, not an arbitrary preference for microservices. I identify what must remain consistent, which team or component owns each decision, and where independent deployment genuinely creates value.
Design the failure model #
Networks delay and duplicate messages. Dependencies become slow before they become unavailable. Processes restart. Operators make mistakes. Architecture must specify timeouts, retries, idempotency, backpressure, degradation, recovery, and the signals that distinguish each failure.
Make data movement explainable #
Every copy of data creates questions: which source is authoritative, how freshness is measured, how corrections propagate, and what happens during replay. I use explicit contracts and delivery semantics so asynchronous systems remain debuggable.
Build observability into the boundary #
Metrics show trends, logs explain events, and traces connect work across boundaries. Useful observability begins with service objectives and failure questions; collecting everything without a diagnostic model creates noise, not understanding.
Architecture areas #
- backend platforms and API design
- distributed workflows, messaging, and event-driven systems
- database architecture and data ownership
- cloud infrastructure, deployment, resilience, and operability
- microservice boundaries and migration from tightly coupled systems
- AI agents, RAG, evaluation, model fallbacks, and cost controls
- security boundaries and production readiness
My engineering notes explain the foundations, the papershelf connects seminal research to production questions, and the blog contains longer implementation-focused work.
From advice to implementation #
Technology should be chosen with intent and built, not just advised. I stay close to the critical path: domain models, APIs, schemas, failure handling, code review, deployment evidence, and operating feedback. This exposes where the original architecture was wrong early enough to change it.
See my work as a CTO in Kolkata, review selected projects, or email basakayush942@gmail.com if you have a system that needs clearer technical direction.