AI Engineering Lead

Hey there! We’re Fever , the world’s leading tech platform for culture and live entertainment. Our mission? To democratize access to culture and entertainment. With our proprietary cutting-edge technology and data-driven approach, we’re revolutionizing the way people engage with live entertainment. Every month, our platform inspires over 300 million people in +55 countries (and counting) to discover unforgettable experiences while also empowering event creators with our data and technology, helping them scale, innovate, and enhance their events to reach new audiences. Our results? We’ve teamed up with major industry leaders like Netflix, F.C. Barcelona, and Primavera Sound, presented international award-winning experiences, and are backed by several leading global investors! Impressive, right? To achieve our mission, we are looking for bar-raisers with a hands-on mindset who are eager to help shape the future of entertainment! Ready to be part of the experience? Now, let’s discuss this role and what you will do to help achieve Fever’s mission. Behind the user-friendly iOS and Android apps and webpage that work across the world is the engineering team. We are in charge of creating, developing, improving, and maintaining all Fever services so that more people can have an amazing experience. About the role AI is changing how product and engineering teams operate, and we're building the team that defines what that looks like at Fever. Your scope is the AI substrate that makes both engineering and product workflows faster, higher-quality, and more leveraged at Fever. You'll lead a team, partner with peers across engineering and product, and report to the Head of Engineering, AI & DevEx. What you'll own Your team . Lead, mentor, and grow a team of AI engineers. The metrics the team is accountable to . Lead time idea→shipped, cost per shipped feature, harness shippable-output rate, human intervention rate. Make the work answerable to numbers. The agentic harness for product and engineering — the skills, sub-agents, guardrails, and verification that make agents produce shippable output, not suggestive output, across the whole software lifecycle. Plus the shared knowledge-base infrastructure these agents read from and write into. You own how good it is and how widely it's adopted. Evaluation infrastructure . A measurement substrate that lets every harness change land on signal, not vibes. The discipline of naked baselines and "ship only on a measured improvement" is non-negotiable. Inference economics . Bound the cost of every AI-driven workflow at Fever. Decide model routing, caching strategy, and when self-hosted open-source wins versus frontier models on the joint quality + cost frontier. About you You're an AI Engineer . You build on top of foundation models — prompt engineering, RAG, agent orchestration, evaluation, fine-tuning when it's the right tool — and you ship those systems to production. You don't need to train models from scratch; you need to make the ones that exist do useful work reliably. You build with agents, you don't just use them . You've authored skills, sub-agents, evals, or verification harnesses yourself. You can ship an experiment one week and ship its replacement the next. Engineering bar . Testing, design patterns, CI/CD, observability — non-negotiable. The harness ships code; the team that builds it has to know what good code looks like. Strong opinions, weakly held. Full ownership. Product sense . You know what makes a phenomenal product experience, and you can imagine the agents that get PMs and designers to the right artefact faster. You don't see "AI for product" as someone else's problem. Builder-leader, not pure manager . You can run a room with executives, the keyboard in a pairing session, and a 1:1 with a senior engineer — all in the same week. You think in measurements . Comfortable with naked baselines, continuous-delivery rules, and killing experiments that don't move the needle. You've been ri