Backend Team Lead (ML)

  • Πλήρης απασχόληση
  • Υβριδικό
  • Λεμεσός, Κύπρος
  • Ανώτερος
  • Μόνιμη
  • Τεχνολογία Πληροφοριών
  • πριν 14 μέρες
Πλεονεκτήματα
  • Ιατρική ασφάλιση
  • Σύνταξη
  • Υποστήριξη μετεγκατάστασης
  • Υποστήριξη για βίζα
  • Προϋπολογισμός για την εκπαίδευση
Αίτηση

Περιγραφή θέσης εργασίας

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

Αρχική δημοσίευση: 8 Ιουλίου 2026 · Τελευταία επιβεβαίωση ενεργής θέσης: 8 Ιουλίου 2026

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