Descripción de la oferta
Overview
In this role, you will lead Fever’s AI engineering team to build the AI substrate that accelerates product and engineering workflows. You’ll partner with peers across engineering and product to ship robust, production-ready AI capabilities, beyond experimental prompts. You’ll own measurable outcomes like time-to-ship and cost per feature, shaping how AI is used across Fever’s software lifecycle. This is a hands-on leadership role in a fast-growing, global company driving transformation through AI. You’ll join a mission-driven team building scalable, high-impact AI tooling for live entertainment.
Compensaciones / Beneficiosbase salary with potential significant bonusstock options40% discount on Fever eventshome office friendlyhealth insurance and flexible remuneration via CobeeEnglish lessons
ResponsabilidadesLead, mentor and grow a team of AI engineersDefine and track team metrics (lead time to ship, cost per shipped feature, harness adoption)Own the agentic harness, including sub-agents, guardrails and verification across the software lifecycleDevelop and maintain evaluation infrastructure and baselines for ignition of AI improvementsDecide on inference economics: model routing, caching, and when to use self-hosted vs frontier modelsEnsure production-readiness with strong CI/CD, observability, and engineering standardsShape product impact by designing AI workflows that accelerate PMs and designers
Requisitos principales5+ years building production software1–2 years (at least) deep in LLM applications, agents, and AI toolingExperience shipping systems on top of foundation models (prompt engineering, RAG, agent orchestration, evaluation)Ability to ship experiments quickly and iteratively replace themStrong emphasis on testing, design patterns, CI/CD, and observabilityBuilder-leader with ability to interact with executives and engineers in the same weekComfort with measurements, naked baselines, and data-driven decision makingUnderstanding of inference economics and model routing/caching decisionsleadership and mentorshipdata-driven decision makingstrong product sensefoundation modelsprompt engineeringRAG (retrieval-augmented generation)
