Secure architecture for auto-scaling AI services

Containerised AI agents on hardened private networking, provisioned as code with no long-lived credentials.

Who it was for: An AI SaaS platform running inference workloads behind a public product.

The problem. AI workloads scale unpredictably and tend to accumulate credentials — API keys in environment variables, secrets pasted into config, services talking over paths nobody documented.

What I did. Provisioned the whole environment as code. Ran the agents in managed containers with auto-scaling, put load balancing in front with a controlled frontend-to-backend path, and moved every credential into managed secrets so nothing long-lived sits in the workload.

The result. A platform that scales with demand and holds no long-lived credentials, with the architecture reproducible from source rather than remembered.