עדיין מחפשים עבודה במנועי חיפוש? הגיע הזמן להשתדרג!

במקום לעבור לבד על אלפי מודעות, Jobify מנתחת את קורות החיים שלך ומציגה לך רק משרות שבאמת מתאימות לך.

מעל 80,000 משרות • 4,000 חדשות ביום
חינם. בלי פרסומות. בלי אותיות קטנות.

חברה בתחום כללי

תל אביב - יפו

חברה בתחום כללי

תל אביב - יפו
מלאה
25,000-35,000 ₪ הערכה מבוססת AI ולא שכר שהתקבל מהמעסיק
הערכה מבוססת AI ולא שכר של המעסיק

we are looking for a Senior DevOps Engineer.
What Youll Do:
Design, scale, and operate high-throughput, low-latency infrastructure supporting RTB bidders and ML prediction services at extreme scale (2Million+ QPS).
Own production GCP environments end-to-end, including deployment, monitoring, 99.999 uptime, incident response, and post-mortems.
Build and maintain infrastructure for massive data ingestion (up to 1TB per hour), continuous ML training pipelines, and real-time prediction systems.
Develop and evolve CI/CD pipelines, DevOps automation, and infrastructure-as-code practices.
Work closely with engineering and data science teams, taking full ownership from design through production and ongoing operations.
Continuously evaluate and introduce improvements to our infrastructure stack, tooling, and operational practices.
Our core stack includes GCP, Kubernetes, Prometheus, Grafana, Python, Go, BigQuery, Redis, Prefect, Aerospike and MySQL. We are looking for someone experienced, independent, and opinionated about production systems, but still curious and eager to improve how things are built and operated.

Requirements:
At least 4 years of experience in a DevOps, Infrastructure or MLOps role preferably in a startup or high-scale environment.
Strong understanding of systems, infrastructure, and how modern distributed applications are built and scaled.
Experience with cloud infrastructure, preferably GCP.
Familiarity with Kubernetes-based production systems and observability tools.
Experience working with Redis and relational or NoSQL databases or data warehouses.
Ability to think in terms of system architecture and long-term scalability, not just short-term fixes.
A strong sense of ownership, urgency, and responsibility for production systems.
Advantages:
Proven experience running ML systems in production.
Experience with high-throughput data pipelines and real-time systems
Background in infrastructure supporting data science or ML teams.

This position is open to all candidates.