All remote jobs

Lead AI Engineer

AI Summary
Fever is seeking a Lead AI Engineer to lead a company-wide AI transformation while remaining deeply involved in implementation. The role is split evenly between discovering operational pain points with business stakeholders and building production AI products such as LLM applications, RAG systems, agents, and chatbots. The successful candidate will architect scalable Python-based services, lead a small engineering squad, and own internal AI tools from prototype through adoption. This is a high-impact leadership role for an engineer who combines strong technical delivery with empathy, operational judgment, and stakeholder communication.
Remote from
Spain
Salary
Undisclosed
Employment
Full Time
Experience
Senior
Published
Apply before
12 Dec 2026
Listing views
15
Application actions
1
Application toolkit

Make your next move.

Prepare your resume, explore your fit, and draft a cover letter for this opportunity.

Opportunity details

About this role.

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.

The Role

We’re looking for a Lead AI Engineer to drive Fever’s company-wide AI transformation. This is a powerful 50% leadership, 50% hands-on building role designed for someone who sits right at the intersection of human operations and advanced AI engineering. We are not looking for a theoretical manager who focus on KPIs, nor a developer who only wants to code in isolation.

We need a hybrid leader. Half of your time will be spent interacting with stakeholders across business domains (Operations, Creative, Support), shadowing them to understand their manual workflows, and identifying where AI can genuinely give them their time back. The other half will be spent in the trenches: designing the technical architecture, writing production code, and building internal AI products—like advanced chatbots, agents, and automated workflows—alongside a small, high-performance squad.The Role

What You’ll Do

  • Stakeholder Management & Discovery (50%): Partner with internal business leaders to audit their current manual processes. Act as a translator: understand their operational bottlenecks, manage expectations on what AI can do, and shape those needs into a clear AI product features.
  • Hands-on Engineering & Architecture (50%): Design, code, and deploy advanced AI systems (LLM applications, agentic workflows, RAG, and custom chatbots) that solve those operational pain points and scale seamlessly.
  • Lead & Mentor: Build and lead a small, agile team of AI engineers. Lead by example by contributing to the codebase, setting high standards for code craftsmanship, and fostering a culture of high-velocity shipping.
  • Own the Product Lifecycle: Take internal tools from early discovery and rapid prototyping to production-grade deployment, ensuring they are intuitive enough for non-technical teams to adopt.

What We’re Looking For

  • The 50/50 Split: You love writing clean, scalable Python code and building AI systems, but you are equally energized by talking to people, understanding their daily operational frustrations, and solving their problems.
  • Proven AI Product Delivery: Deep, hands-on experience shipping AI systems in production (LLM frameworks like LangGraph, LlamaIndex, or agentic automation) — ideally tools or chatbots that have actively optimized human workflows.
  • Strong Operational Sense: You can naturally look at a messy, manual process and instantly visualize how an AI product could streamline it to save hours of manual labor.
  • Solid Engineering Foundations: Strong software engineering background (APIs, microservices, databases, AWS ecosystem) combined with a pragmatic, outcome-driven mindset.
  • Empathy & Communication: Exceptional ability to build relationships with non-technical stakeholders, navigate ambiguity, and align engineering efforts with operational needs.

The environment

  • Product-driven, action-oriented, and fast-moving. We value shipped code and tangible internal time-savings above all.
  • We hire builders with empathy: people who own systems end-to-end, love solving human operational chaos, and turn it into smooth, AI-powered workflows.
  • Leadership is collaborative, highly communicative, and technically sharp.
  • This role is based in Spain.

Benefits & Perks

  • Responsibility from day one and professional and personal growth in one of the most exciting AI roles in the company.
  • Opportunity to have a real impact in a high-growth global category leader.
  • A compensation package consisting of base salary and the potential to earn a significant bonus for top performance.
  • Stock options plan.
  • 40% discount on all Fever events and experiences.
  • Based in Madrid, with a home office friendly setup.
  • Health insurance and other benefits such as Flexible remuneration with a 100% tax exemption through Cobee.
  • English / Spanish lessons.
  • Wellhub membership.
  • Possibility to receive in advance part of your salary by Payflow.

Thank you for considering joining Fever. We cannot wait to learn more about you!

If you want to learn more about us: Fever’s Blog | Tech.Eu |TechCrunch

Fever is committed to creating an inclusive and diverse workspace where everyone’s background and ideas count. Our main goal is to find the best possible talent regardless of place of birth, racial or ethnic origin, gender, gender identity, religion, opinion, sexual orientation, disability, pregnancy, marital status, age or caring responsibilities. We encourage everyone to apply!

If you require any kind of accommodation during the selection process please contact our Talent team so we can help you by providing a welcoming and seamless journey.

If you want to know more about how Fever processes your personal data, click here Fever – Candidate Privacy Notice

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

5/5
IndependentCollaborative
AI insightThe position requires both senior-level AI engineering expertise and the ability to lead discovery, architecture, delivery, and team execution. It demands production experience with rapidly evolving AI technologies while aligning solutions to ambiguous operational needs across multiple business functions.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$120,000
US market range (per year)$95k–$150k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary range is disclosed. The annual compensation figures are estimated USD market benchmarks for a senior AI engineering lead role with hands-on LLM, architecture, stakeholder management, and team leadership responsibilities; yearly cadence is inferred from the full-time professional role.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
Describe an AI product you delivered from discovery through production that improved an operational workflow.

I would explain the original workflow, quantify its bottlenecks, describe how I validated the user need, and outline the architecture and rollout. I would include adoption, quality, latency, cost, and time-saved metrics, as well as iterations made from user feedback.

How would you decide whether a manual business process should be automated with an LLM, traditional software, or not automated at all?

I would first map the workflow, input quality, decision risk, volume, and expected business value. Deterministic and stable tasks are usually better served by conventional automation, while unstructured language-heavy work may benefit from LLMs with guardrails; high-risk processes require human review or may not be suitable for automation.

How would you design a reliable RAG-based internal assistant for non-technical employees?

I would establish governed source content, document ingestion and chunking standards, embedding and retrieval evaluation, citation requirements, access controls, and observability. I would also implement fallback behavior, human escalation, feedback collection, and offline evaluation sets before broad deployment.

How do you lead a small AI engineering team while continuing to contribute hands-on?

I set clear technical and product outcomes, define ownership boundaries, and use architecture reviews and lightweight delivery rituals to maintain quality. I reserve focused time for critical implementation work while coaching engineers, removing blockers, and ensuring decisions remain tied to user impact.

What metrics would you use to evaluate an internal AI workflow product?

I would track adoption, task completion rate, time saved, error or escalation rate, user satisfaction, model quality, latency, availability, and cost per successful task. The exact metric set should connect directly to the operational problem identified during discovery.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Next step

Apply now.

Follow the employer’s application method and review Jobicy’s safety guidance before sharing personal information.

Did you apply?Let us know, and we’ll help you track your application.

Continue on the employer website

Protect your personal information and never pay to secure an interview or job offer. .

Log in to save
One quick step before you apply

Sign in to continue.

Sign in or create a free account to continue to the employer's application.

Add alert
Jobs Talent AI Tools Salaries
Menu