Python
Python Backend Architecture
The backend is where products quietly succeed or fail. I design and build Python services with clear domain boundaries, predictable performance and the operational tooling a growing team needs, whether that is a Django platform, a Flask service or a high-throughput async FastAPI API.
Core
DjangoFlaskFastAPI
Also in the toolbox
PostgreSQLRedisCelerySQLAlchemyPydanticDockerAWS / GCPGraphQLWebSocketspytest
What you get
Deliverables, not just hours.
01System architecture & data model
02REST / GraphQL APIs with OpenAPI docs
03Background jobs, queues and schedulers
04Auth, RBAC and multi-tenancy
05Performance profiling & query optimisation
06CI/CD, containerisation and monitoring
Typical use cases
Where this creates value.
SaaS platform core
Multi-tenant Django or FastAPI backend with billing, roles and audit trails.
High-throughput APIs
Async FastAPI services with caching, rate limits and horizontal scaling.
Legacy modernisation
Untangle a Flask or Django monolith into well-bounded modules without downtime.
Related work
Python in practice.
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