API & Backend Engineering

Production-grade APIs, auth systems, database architecture, and deployment —
engineered to be reliable, documented, and maintainable.

Most backend problems are not hard to solve. They are hard to solve well — with
the right data model, sensible API contracts, proper authentication, and a
deployment that does not require heroics to maintain. I design and build backends
that engineering teams can understand, extend, and operate without the original
author on speed dial.

I work across three backend stacks and choose based on what the problem actually
needs. Node.js with Express for JavaScript-native services and real-time workloads.
FastAPI for Python-first teams that need automatic API documentation and high
throughput. Spring Boot for enterprise Java environments that require structure,
Hibernate ORM, and JPA-managed persistence. If you have an existing stack, I
adapt to it. If you are starting fresh, I will recommend the right one for
your use case and team.

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What's included

RESTful API design and implementation
Resource-oriented endpoints, consistent naming conventions, appropriate HTTP
methods and status codes, pagination, filtering, sorting. API contracts designed
to be stable — versioned where breaking changes are likely, documented as they
are written. Postman collections included. I have designed and shipped APIs
across all three of my backend stacks and know what makes an API pleasant to
consume versus painful.

Authentication and authorisation systems
JWT with refresh token rotation. OAuth 2.0 — Google, GitHub, or any provider
with a standards-compliant flow. OTP verification via email or SMS. WebAuthn for
passkey-based authentication. Session-based auth for server-rendered applications.
Role-based access control (RBAC). Secure token storage, HTTPS enforcement,
rate limiting on auth endpoints. Authentication is the part of a backend that
hurts most when it is wrong — I treat it accordingly.

Database architecture and query optimisation
Schema design from first principles for both relational and document databases.
PostgreSQL and MySQL with proper normalisation, foreign key constraints, indexed
columns, and query plans that do not fall apart under load. MongoDB for document
workloads with embedded vs referenced documents decided deliberately. Firebase
Realtime Database and Firestore for real-time sync. Supabase for managed Postgres
with Row Level Security. SQLAlchemy ORM in Python stacks, Hibernate and JPA in
Java, Mongoose in Node. Migrations managed, not manual.

Real-time features
WebSocket servers, Firebase Realtime Database listeners, Server-Sent Events for
live dashboards and notifications. I have shipped a real-time PWA with live
Firebase sync into production and know the edge cases around connection drops,
reconnection logic, and offline queue management.

Spring Boot and Java backend development
Spring Boot application setup, Spring MVC for web layer, Spring Security for
authentication, Hibernate and JPA for persistence, Maven and Gradle for build
management, JDBC for cases where the ORM is overkill. Enterprise-grade structure
without enterprise-grade ceremony.

Python backend development
Django for full-featured web applications with admin, ORM, and batteries included.
Flask for lightweight services and REST APIs. FastAPI for async, high-performance
APIs with automatic Swagger and ReDoc documentation. SQLAlchemy for ORM, Pandas
for data-adjacent workloads, BeautifulSoup and Scrapy for data collection
pipelines. I have built and deployed Python backends in production across all
three frameworks.

Data processing and NLP pipelines
Multi-stage data pipelines in Python — ingestion, cleaning, transformation, and
output. NLP workflows including tokenisation, n-gram extraction, keyword frequency
analysis, and entity recognition. Pandas and NumPy for numerical and tabular work.
CSV export, REST API output, and database persistence as delivery formats. I built
KeyFinder — a LinkedIn scraper and NLP keyword analyser — as a production web
app, not a notebook experiment.

Docker and containerisation
Dockerfiles, docker-compose for local development parity, container-based
deployment. Images kept small, layers cached intelligently, environment variables
managed through .env and secrets, not hardcoded.

CI/CD pipeline setup
GitHub Actions workflows for test, lint, build, and deploy. Branch protection
rules, pull request checks, environment-specific deployments. Continuous delivery
to Vercel, Railway, Render, Netlify, or AWS EC2. Pipelines that make deployment
boring — which is exactly how it should be.

API testing and documentation
Postman collections for manual and automated testing. Automated test suites for
critical endpoints. FastAPI's automatic OpenAPI documentation. README-driven
documentation for every API I ship. A backend is only as good as its documentation
— I write it as part of the work.

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Good fit if you need

A backend for a mobile app. A microservice carved out of a monolith. A data
ingestion and processing API. An authentication system built properly from the
ground up. A real-time feature added to an existing product. A Spring Boot
service for an enterprise Java environment. A Python data pipeline exposed as
a REST API. A legacy backend rewritten to something the next developer can
understand.