Ai Engineering Lead

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 / Beneficios base salary with potential significant bonus stock options 40% discount on Fever events home office friendly health insurance and flexible remuneration via Cobee English lessons Responsabilidades Lead, mentor and grow a team of AI engineers Define 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 lifecycle Develop and maintain evaluation infrastructure and baselines for ignition of AI improvements Decide on inference economics: model routing, caching, and when to use self-hosted vs frontier models Ensure production-readiness with strong CI/CD, observability, and engineering standards Shape product impact by designing AI workflows that accelerate PMs and designers Requisitos principales 5+ years building production software 1–2 years (at least) deep in LLM applications, agents, and AI tooling Experience shipping systems on top of foundation models (prompt engineering, RAG, agent orchestration, evaluation) Ability to ship experiments quickly and iteratively replace them Strong emphasis on testing, design patterns, CI/CD, and observability Builder-leader with ability to interact with executives and engineers in the same week Comfort with measurements, naked baselines, and data-driven decision making Understanding of inference economics and model routing/caching decisions leadership and mentorship data-driven decision making strong product sense foundation models prompt engineering RAG (retrieval-augmented generation)