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Engineering Manager - Edge AI

CompraTica Empleos

EMP:Technology
Berlin
Tiempo Completo
Remoto
0 vistas

Descripción

<p>Our AI Platform & Edge team sits at the heart of how SumUp is scaling intelligent, merchant-facing experiences across millions of interactions.

We build and operate the AI systems — from LLM-powered assistants to agentic platforms — that directly shape how merchants get support, resolve issues, and engage with SumUp's products.

As Engineering Manager for Edge, you'll take ownership of a growing, cross-functional team of engineers and ML specialists, leading the execution of some of our most technically complex and commercially critical AI initiatives.

This is a rare opportunity to lead meaningful AI work at scale — not just shipping features, but shaping how an entire platform evolves.

</p> <h2>What you'll do</h2> <ul> <li> <p>Lead the day-to-day execution of the Edge AI team, translating long-term product vision into clear sprint roadmaps and measurable delivery milestones</p> </li> <li> <p>Own the migration of our AI assistant to an autonomous agentic setup, maintaining performance while expanding AI support across all merchant-facing channels</p> </li> <li> <p>Set the technical direction for LLM-powered system design, making clear tradeoffs across quality, latency, and cost</p> </li> <li> <p>Build a data-informed engineering culture by defining performance metrics, tracking delivery velocity, and using insights to continuously improve team health and throughput</p> </li> <li> <p>Hire, mentor, and develop a team of engineers and ML specialists — including interns — creating an environment where people grow and do their best work</p> </li> </ul> <h2>You'll be great for this role if…</h2> <ul> <li> <p>Strong hands-on background in ML/AI engineering or data science, with experience leading a team delivering production AI products</p> </li> <li> <p>Deep technical knowledge of LLM-powered system design, including RAG, embeddings, tool/function calling, and orchestration frameworks such as LangChain or LlamaIndex</p> </li> <li> <p>Proficiency in Python and core.

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