Job Description

***Hybrid, 3 days onsite, 2 days remote***

***We are unable to sponsor as this is a permanent full-time role***

A prestigious company is looking for a Site Reliability Engineer. This role is focused on observation, logging, and capacity planning. This engineer will need experience/exposure to Linux systems, Kubernetes/Docker, Terraform, Jenkins, Ansible, Harness, and Kafka.

Responsibilities:

• Collaborate with development, operations and infrastructure teams to ensure availability of services, and to work through implementation issues
• Develop automation for incident response and to prevent problem recurrence
• Create and enhance runbooks to respond to service outages or degradations
• Assess the production readiness of services
• Define and track operational metrics for production performance, reliability, scalability and availability
• Architect, develop and maintain shared services and tools to improve reliability and reduce toil across the organization

Qualifications:

• Bachelors or Masters Degrees in Computer Science, Information Systems or other related field, or equivalent work experience
• Minimum of 4+ years of experience in Site Reliability Engineering / DevOps
• Experience with maintaining and troubleshooting large-scale distributed systems
• Experience managing infrastructure in public cloud environments like AWS (preferred), Azure or GCP
• Experience with AIOps and predictive analysis for anomaly detection, forecasting system capacity using monitoring and alerting tools like Splunk, AppDynamics, Datadog, StackDriver, Sysdig, Prometheus or Grafana
• Programming/scripting experience in languages like Java, Bash, Python or Go
• Experience with distributed messaging systems like Kafka, RabbitMQ, or ActiveMQ
• Experience with container orchestration systems like Kubernetes, Mesos, Docker Swarm or Rancher
• Experience with using Continuous Integration and Continuous Delivery (CI/CD) tools like Jenkins, Travis, Harness, Appveyor, CodeBuild or CodePipeline
• Familiarity with leveraging large language models (LLMs) to automate and optimize SRE workflows. This may include using AI-powered tools to perform tasks such as, writing scripts, summarizing incident reports, or even creating and maintaining AI workloads.

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