Senior 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.
Responsibilities
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
Requirements
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)
Nice to have
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