Zapier
Mid
Applied AI Engineer building Zapier's shared AI Platform — LLM proxy, observability, and MLOps tooling used company-wide
Interview for an Applied AI Engineer role on Zapier's AI Platform team, building the shared LLM Ops and ML Ops infrastructure — proxy servers, observability, evaluation tooling — that other product and engineering teams rely on. Expect questions on production LLM/ML operations, platform engineering trade-offs, and working in TypeScript and Python at the intersection of infra and applied AI.
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What this interview tests
- LLM Ops / ML Ops in production
- Building shared/reusable platform infrastructure (proxy servers, tooling, APIs)
- Observability, monitoring, and evaluation workflows for AI systems
- Engineering trade-offs: reliability vs. latency vs. cost vs. quality
- TypeScript and Python backend development
- Collaborating across engineering teams to drive platform adoption
Common question themes
Describe a production AI/ML system you built or operated, and how you handled reliability and cost trade-offs
How would you design an LLM proxy server that many internal teams depend on?
Walk through building an evaluation or observability pipeline for a model in production
How do you make an internal platform capability easy for other engineering teams to adopt?
Experience with the full lifecycle: building, testing, deploying, and scaling an ML/LLM architecture
How do you decide when to incorporate an emerging AI tool or pattern into a shared platform versus staying with the status quo?
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