Full-Stack Python Development

Who this is for

Founders and engineering leaders who want to skip the agency overhead and ship a real product.

What problem this solves

Hiring two contractors (frontend + backend) means coordinating two timelines, two opinions on contracts, and two people pointing at each other when something breaks. A single full-stack Python engineer ships faster and owns the whole stack.

What you get

Working SaaS or internal tool, deployed to your cloud
FastAPI or Flask backend with typed schemas, auth, and migrations
React + TypeScript frontend (Tailwind + shadcn/ui)
CI/CD, observability, and a deployment runbook

How the engagement runs

Design. Whiteboard the data model, the auth model, and the 5 happy-path flows.
Walking skeleton. Login → core action → success in week one. Unstyled, but real and deployed.
Build. Vertical slices, weekly demos, no surprises.
Polish & ship. Auth hardening, observability, performance, runbook, and handoff.

Deliverables

Source code in your repo with PR-based history
Backend: FastAPI/Flask + Postgres + Alembic migrations
Frontend: React + TypeScript + Tailwind + shadcn/ui
Auth (Supabase, Auth0, Clerk, or custom)
CI/CD (GitHub Actions → Vercel/Fly/Railway/AWS)
Sentry-style error monitoring + uptime checks

Outcomes you can expect

First production deploy in week 1
Clean, typed code your future team can read
No 'agency black box' — every commit is yours, every decision is documented

Pricing & timeline

MVP builds $10K–$35K USD fixed-scope. Ongoing development $4K–$12K USD/month.

MVP: 4–10 weeks. Ongoing: monthly retainer with sprint cadence.

Tech stack

Backend: Python 3.11+, FastAPI, Flask, SQLAlchemy, Alembic
Frontend: React 18, TypeScript, Vite, Tailwind, shadcn/ui
Data: PostgreSQL, Redis, Supabase, MongoDB
Async: Celery, RQ, BullMQ
Deploy: Vercel, Fly.io, Railway, AWS, GCP
Observability: Sentry, Logtail, Grafana

Relevant case studies

Enterprise Data Pipeline & Analytics Engine — A production-grade data engineering pipeline processing 10M+ records daily with automated ETL workflows, real-time analytics, and comprehensive business intelligence reporting.
Intelligent Web Scraping & Lead Enrichment Platform — A distributed web scraping system extracting and enriching business data from 50+ sources with anti-detection, proxy rotation, and intelligent rate limiting.
Deep Learning Image Classification & Object Detection API — A production ML API for image classification and object detection using PyTorch and Keras, deployed with FastAPI and Docker for scalable inference.
Real-Time Analytics API with Flask & NoSQL — A high-performance Flask REST API for real-time event tracking and analytics, backed by MongoDB and Redis for sub-millisecond query responses.

Frequently asked questions about full-stack Python

Why pick a full-stack Python developer over a separate frontend + backend hire?

Speed and accountability. One person making coherent decisions across the stack ships faster than two coordinating handoffs, and there's no 'that's the other team's bug' when something breaks.

Do you only do FastAPI/Flask, or also Django?

FastAPI and Flask are the default. We can work in Django if your team already runs it; the patterns translate but Django's conventions add weight that small teams rarely need.

Can you also do Node.js or Next.js?

Yes — we've shipped 3000+ Next.js sites in an automation pipeline, and most of our recent SaaS work uses TypeScript end-to-end. Pick Python for ML/data-heavy backends, TS for product-velocity backends.

Will you work with our existing codebase?

Yes. We start with a 1-day codebase audit and map our work to your existing patterns rather than imposing ours.

What about testing?

Pytest + Playwright for end-to-end. We don't write tests for the sake of coverage — we test the contract boundaries (API shapes, auth, money math) and the flows real users take.

Do you handle deployments and on-call?

We set up CI/CD and observability so your team can. Optional 30-day on-call window for critical post-launch issues.