Hire · Freelance & consulting
Hire a Django developer who has put real-time AI models behind an API
Django is half of my daily backend stack, next to FastAPI. I built the Django REST backend that serves the speech emotion recognition model in real time, the Django backend for the Smart India Hackathon CCTV analytics platform, and Python APIs on FastAPI and Django at Zuneko Labs.
Problems I solve with this stack
A trained model has to serve live requests, not notebook calls.
A Django REST backend around the model with a real-time inference path, as built for the speech emotion recognition system (93.27% accuracy across four datasets).
Video or AI processing blocks web requests.
Background workers next to the API: the CCTV analytics backend paired Django APIs with workers for real-time video analysis and alert dispatch.
The data model has outgrown its first version.
Planned migrations with a rollback path, and data in the right store: PostgreSQL for relational data, MongoDB for event-shaped data such as detections.
The admin and the API disagree about the rules.
Put validation in serializers and model constraints, so the Django admin, the REST API and background jobs all enforce the same rules on the same data.
What you get
- Django REST APIs with documented endpoints and validated inputs.
- Model-serving endpoints, with Celery + Redis workers for slow work.
- PostgreSQL schema design and migrations.
- Admin views your team can use to manage the data.
- Docker, CI and tests, plus a handover walkthrough.
How a Django engagement runs
1. Define what the model must serve
Input format, latency target and who calls it, so the endpoint is designed around real usage.
2. Build the REST endpoints
Validated inputs, documented responses, and the model loaded once per worker process instead of on every request.
3. Move slow work off the request
Celery + Redis workers for video or inference jobs, with data in the right store: PostgreSQL for relational data, MongoDB for event-shaped data.
4. Admin, tests and handover
Admin views for your team, Docker, CI and tests, then a walkthrough covering migrations and deployment.
Frequently asked questions
Can Django serve machine-learning models in real time?
Yes. Load the model once per worker process, keep slow inference off the request thread, and return results through a REST endpoint: the setup behind the speech emotion recognition backend.
Can you add AI features to our existing Django app?
Usually without a rewrite: model-serving endpoints, LLM integrations or background processing can sit alongside your current apps and share the same database and auth.
Django or FastAPI for a new project?
Django if you want the ORM, admin and auth built in. FastAPI for lean async services and webhook receivers. Some systems use both: Django for the product and a small FastAPI service for inference.
Can non-technical staff manage data through the admin?
Yes. Django admin views let your team review and correct records without a separate tool, with permissions that limit who can change what.
Do you work with international clients?
Yes. I'm based in Pune, India, and work with teams in India and remotely worldwide. Communication is async-first with scheduled calls when needed.
What does an engagement look like?
A short call to scope the problem, a written scope (what will be built, what won't, and how we'll know it works), then small milestones you can review, with the code in your repository from day one.
More questions? Ask directly
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