AI Engineer
🔥 Hotengineering
You own the full lifecycle, from prototyping with foundation models to hardening retrieval-augmented generation pipelines for production traffic. The work is mostly glue code. LLM APIs. Vector databases. Backend services. On top of that sit the dashboards that track cost and latency against output quality. The real craft is the eval loop. Product managers and data teams lean on this role to turn model capabilities into reliable, user-facing features.
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A Day in This Role
Overnight eval metrics. Error logs from production pipelines. The first hour goes to reading what broke and what held. Pairing with a product manager on a new retrieval flow takes most of the afternoon, ending with a PR that swaps the embedding model. The day closes in a design review where the team argues about prompt strategy and guardrails until someone has to leave for dinner.
Common Interview Topics
- 01Design a RAG pipeline for a customer support chatbot, walk through embedding model selection, chunking strategy, and retrieval approach
- 02Compare latency-cost tradeoffs between hosting your own open-source model versus calling a managed LLM API for a real-time feature
- 03Describe how you would implement guardrails to prevent prompt injection in a user-facing AI feature
- 04Walk through your approach to evaluating LLM output quality, what metrics do you track and how do you build an eval suite?
- 05Design a system that gracefully degrades when your LLM provider has an outage mid-request
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Career Path
AI Engineers typically advance into AI Solutions Architect or Staff AI Engineer roles, and some move laterally into AI Product Management where their deep technical context becomes a strategic advantage.