Case Study

Async AI Inference Backend

Problem

A client needed a backend architecture for long-running AI jobs that could accept API requests, queue work for background processing, store results, track status, and deliver results via callbacks and webhooks.

Approach

Designed an async job processing architecture with FastAPI endpoints for job submission, RabbitMQ/Redis for queuing, worker processes for inference, object storage (S3/GCS) for result persistence, and webhook callbacks for result delivery. Implemented status tracking and error handling throughout the pipeline.

Key Decisions

• Decoupled submission from processing via queue for scalability
• Object storage for results to avoid database bloat from large inference outputs
• Webhook callbacks for real-time result delivery to integrating systems

Tech Stack

Python, FastAPI, Flask, Redis, RabbitMQ, Docker, AWS, GCP, S3/GCS, webhooks.

Outcome

Reusable async inference pattern deployed across multiple AI projects at Infidea.

Role

Designed and built the full architecture.

CTA

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