techruiter. is hiring a
Site Reliability Engineer (SRE) in United Kingdom

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Site Reliability Engineer (SRE)
🏢 techruiter.
💵 ~$117k-$210k
📍United Kingdom
📅 Posted on Jun 11, 2024


The job is for a Site Reliability Engineer to maintain the stability and efficiency of LLM and Machine Learning platforms by collaborating with cross-functional teams, designing and automating infrastructure, managing deployment pipelines, implementing monitoring systems, leading incident response efforts, performing capacity planning, ensuring security and compliance, continuously improving system reliability, and maintaining documentation.


  • Bachelor's or Master's degree in Computer Science, Information Technology, or a related field
  • Proven experience as a Site Reliability Engineer or a related role with a focus on LLM and Machine Learning infrastructure
  • Strong proficiency in cloud platforms (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes)
  • Experience with configuration management tools (e.g., Ansible, Terraform) and CI/CD pipelines
  • Knowledge of monitoring and observability tools (e.g., Prometheus, Grafana, ELK Stack)
  • Scripting and automation skills (e.g., Python, Bash)
  • Excellent problem-solving and troubleshooting skills
  • Strong communication and collaboration skills


  • Infrastructure Design and Automation: Collaborate with engineering and research teams to design, implement, and automate infrastructure for LLM and Machine Learning workloads
  • Deployment and Configuration: Manage deployment pipelines, configuration management, and orchestration tools to streamline the deployment of models and services
  • Monitoring and Alerting: Implement and maintain robust monitoring, alerting, and logging systems to proactively identify and resolve issues
  • Incident Response: Lead incident response efforts, investigate root causes of outages, and implement preventive measures to reduce the likelihood of recurrence
  • Capacity Planning: Perform capacity planning and scaling to accommodate growing workloads and ensure resource efficiency
  • Security and Compliance: Collaborate with security teams to implement security best practices, vulnerability assessments, and compliance requirements for LLM and Machine Learning systems
  • Continuous Improvement: Continuously evaluate and improve system reliability, performance, and efficiency through automation and optimization
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