Cloud Infrastructure Engineer - LLM Routing & Cost Optimization

We're looking for someone genuinely strong on Google Cloud Platform - not necessarily years of work experience, but real depth: you've built things on GCP, you understand its IAM, quota, and networking model cold, and you can reason about Cloud Run, Vertex AI, and multi-project setups from first principles. You'll design and build Zeqo's multi-provider LLM router - the internal system that decides which model/provider handles each request (Google Gemini, OpenAI, Anthropic, and self-hosted open-weight models), optimizes for cost/latency/quality, and aggressively reduces redundant token spend through prompt caching and smart request management. This is a foundational infrastructure role: get it right and every downstream product gets faster and cheaper automatically.

This is intentionally open to early-career engineers — we care far more about depth of GCP knowledge and evidence of things you've actually built than a resume with years on it.

*Location: Fully Remote*

What You'll Do

• Design and build a unified LLM router that abstracts Google, OpenAI, Anthropic, and self-hosted model endpoints behind a single internal API, so product teams never hardcode a provider.
• Implement intelligent routing logic — route by task type, cost ceiling, latency budget, context length, or model capability (e.g., OCR evaluation vs. conversational tutoring vs. embedding generation).
• Build fallback and failover chains so a rate limit, timeout, or outage on one provider automatically retries…

Skills

• GCP
• LLMs
• Machine Learning
• Redis