This is the part employers care about most and the thing that makes you stand out as an AI engineer: LLMOps. You already know deployments, so what you're adding is the AI-specific layer on top. That means rate limiting, because these APIs have hard limits. Cost optimization, model routing, and caching, so you're not running the same prompts over and over.
Monitoring and observability, so you can make things deterministic, catch what's failing repeatedly, and actually see inside the system to know what the AI is doing. And evaluations, so you can measure whether your system got better or worse when you changed something. Being able to build the system is one thing.
Being able to deploy it is where the real gap is right now.
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