Spotify - Engineering Manager - AI Fleet Management & Honk
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Requirements
• You have 3+ years of experience leading engineering teams working on distributed systems or developer platforms at scale • You have 5+ years of experience in backend engineering with strong expertise in Java, Python, and/or Node.js • You have experience integrating AI/ML systems, LLMs, agent frameworks, or AI-assisted tooling into real-world systems • You understand how autonomous systems make decisions and how to design guardrails that ensure safety, reliability, and traceability • You think in systems, considering data flows, model behavior, orchestration layers, and operational impact • You are comfortable leading in emerging technical domains where patterns are still forming • You create inclusive environments where diverse perspectives strengthen technical and product decisions • You communicate clearly across engineers, product leaders, and executives about both the opportunities and risks of AI-driven systems • Where You'll Be • We offer you the flexibility to work where you work best! For this role, you can be within the EMEA region as long as we have a work location (excluding France due to on-call restrictions). • This team operates within the Central European and GMT time zone for collaboration • We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us. Find our AI notice here: https://lifeatspotify.com/ai-notice
Responsibilities
• Lead and grow a team building AI-native backend systems that orchestrate large-scale, autonomous fleet changes • Define the technical vision for integrating LLM-powered reasoning and agent-based execution into production infrastructure • Architect systems where AI agents analyze service metadata, generate safe change plans, execute updates, and validate outcomes • Establish safety boundaries, evaluation frameworks, and observability models for AI-driven automation at scale • Drive responsible AI adoption by embedding governance, auditability, and human-in-the-loop safeguards into system design • Balance rapid experimentation with platform reliability, ensuring intelligent automation meets production-grade standards • Partner with product and go-to-market teams to position Portal and AI fleet management as a leading developer platform • Build a team culture grounded in curiosity, technical rigor, and continuous learning in emerging AI systems
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