Back to SearchSenior Site Reliability Engineer
We are currently seeking an experienced Senior Site Reliability Engineer (SRE) to join our team.
In this critical role, you will collaborate closely with software developers and operations teams to ensure the high reliability, scalability, and efficiency of our systems. You will also strongly focus on meeting and exceeding customer expectations. Your expertise will be crucial in deploying, maintaining, and automating our infrastructure and application environments to ensure seamless user experiences.
Your proactive involvement will be key to enhancing system reliability, optimizing resource utilization, and ensuring continuous improvement in our operational practices.
Your responsibilities will include defining and tracking Service Level Objectives (SLOs), managing error budgets, and reducing toil through automation. You will play a pivotal role in driving the success of technology initiatives, maximizing their impact across the organization, and ensuring that solutions consistently meet the high standards our customers expect.
- Collaborate with development, security, quality, and operations teams to implement SRE practices and ensure system reliability
- Define and support the required level of reliability, availability, and performance for services and applications
- Troubleshoot, mitigate, and support fixing of the infrastructure and application issues in a timely manner
- Implement a monitoring system for the infrastructure and application reliability
- Bachelor’s degree in Computer Science, Engineering, or a related field
- Proven experience in any cloud (AWS/GCP/Azure)
- Experience with implementing SRE practices such as SLO/SLI, Error budgets, Postmortems, Reducing Toil, capacity planning, and Incident Management
- Knowledge of Python or other scripting/programming language
- Strong background in monitoring tools
- Proficiency in CI/CD tools, infrastructure as code, and configuration management
- Solid knowledge of container orchestration technologies (Kubernetes, Docker)
- Expertise in deployment and management of LLMs, including technologies like RAG
- Certification in Kubernetes, AWS/GCP/Azure, or similar technologies
- Proven experience in DevOps
- Knowledge of managing and optimizing AI/ML models in production environments, including basic deployment, monitoring, and maintenance
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