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Site Reliability Engineer - Remote

Posted 3 days ago
DevOps / Sysadmin
Full Time
UK
£70,000 - £90,000/year

Overview

As a Site Reliability Engineer at Zefr, you’ll apply your expertise in cloud infrastructure, CI/CD, Observability, and core SRE concepts, to deliver high-quality, reliable, and scalable solutions.

In Short

  • Support and build systems and tools for deployment and management of product features.
  • Deploy and support a multi-cloud, micro-service architecture.
  • Collaborate to architect secure and scalable applications in AWS and GCP.
  • Encourage continuous improvement across engineering teams.
  • Maintain the health of production environments.
  • Participate in 24/7 on-call rotation.
  • Debug code at application and infrastructure level.
  • Mature CI/CD workflows and release process.
  • Propose and review Engineering Request for Comments (RFC).
  • Research and propose new solutions.

Requirements

  • 4+ years of experience in Cloud Infrastructure.
  • Experience with Kubernetes and container workloads.
  • Knowledge of GitOps and CI/CD pipelines.
  • Experience with IaC tools like Terraform.
  • Strong problem-solving skills focused on automation.
  • Experience with Monitoring and Observability tools.
  • Understanding of Cloud Networking concepts.
  • Strong communication and documentation skills.

Benefits

  • Monthly allowance for health care and dental.
  • Pension Scheme with 3% company contribution.
  • 28 days of holidays per year.
  • Flexible hybrid work schedule.
  • Summer Fridays.
Zefr logo

Zefr

Zefr is a leading data company focused on enabling responsible marketing within social media environments. With a strong emphasis on understanding content across major platforms like YouTube, Meta, TikTok, and Snap, Zefr leverages patented AI technology to provide brands and agencies with accurate and transparent targeting and measurement solutions. Headquartered in Los Angeles, California, Zefr operates globally, fostering a culture of innovation and collaboration in the fields of machine learning, computer vision, and natural language processing.

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