Backend Team Lead (ML)

  • Vollzeit
  • Hybrid
  • Limassol, Zypern
  • Senior
  • Unbefristet
  • Informationstechnologie
  • vor 14 Tagen
Vorteile
  • Krankenversicherung
  • Rente
  • Umzugsunterstützung
  • Unterstützung bei der Visumbeantragung
  • Bildungshaushalt
Bewerben

Stellenbeschreibung

Mayflower is a technology company building highload products used by millions of people worldwide. Operating at the scale of one of the world's top-50 websites, we solve complex engineering challenges and create solutions that power real-time entertainment for a global audience.

We are looking for a Backend Team Lead for our ML development team focused on building and delivering production-grade ML-powered applications.

This role is not about research or experimentation. You will be responsible for turning ML models into reliable, scalable, user-facing services.

You will work at the intersection of machine learning and backend engineering, owning the full lifecycle of ML-powered features — from integration to deployment and operation in production.

Job Responsibilities

  • Design and build production ML services (APIs, microservices, real-time systems)

  • Lead and grow a team of backend engineers: plan work, mentor team members, conduct code reviews, provide technical guidance, and foster engineering excellence.

  • Drive architectural decisions and ensure the scalability, reliability, and maintainability of ML services.

  • Integrate ML models into user-facing applications

  • Ensure reliability, scalability, and performance of ML-powered systems

  • Define and implement best practices for serving, versioning, and monitoring models in production

  • Collaborate closely with:

    • ML engineers / data scientists (who develop models)

    • DevOps / MLOps team (who provide platform and infrastructure)

  • Own the delivery pipeline of ML features into production

  • Lead and mentor engineers working on ML-powered applications

  • Drive architectural decisions around low-latency and high-load systems

  • Identify bottlenecks between experimentation and production and eliminate them

Ursprünglich veröffentlicht: 8. Juli 2026 · Zuletzt als aktiv bestätigt: 8. Juli 2026

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