Applied AI engineering for AI products

Applied AI engineering sits close to the actual product problem. The work is not only writing backend code or connecting an AI provider; it is translating requirements into something people can use.

That means handling the unglamorous parts too: authentication, billing, data models, environment configuration, deployment failures, integration edge cases, and the operational details that decide whether a product survives first contact with reality.

For AI products, that role matters because demos are easy to overbuild and systems are easy to underbuild. The useful middle is turning product intent into deployed infrastructure.