Lead Site Reliability Engineer - Network

Consistently models and champions site reliability culture and practices, documents and shares knowledge within your organization via internal forums and communities of practice

Leads initiatives to improve the reliability and stability of your team’s applications and platforms using data-driven analytics to improve service levels, proactively identifying and solving technology-related bottlenecks in areas of expertise

Drives collaboration with your team to identify comprehensive service level indicators and the stakeholder partners to establish reasonable service level objectives and error budgets with your customers

Uses enterprise-authorized AI capabilities within the work environment to accelerate major-incident triage, troubleshooting, and post-incident analysis, validating outputs and handling operational data according to sensitivity and security requirements.

Serves as the main point of contact during major incidents for your application and has the skills to identify and solve the issue quickly to avoid financial loss to the business

Offers a high level of technical expertise within one or more technical domains and provides advice and mentorship to other engineers

Leads reuse-first adoption of AI-assisted reliability workflows across SDLC/toolchain practices (e.g., CI/CD quality checks, test/validation automation, and operational readiness), ensuring traceability/auditability, resiliency, and security controls.

Formal training or certification on site reliability engineering concepts and 5+ years applied experience

Demonstrated proficiency in reliability, scalability, performance, security, enterprise system architecture, toil reduction, and other site reliability best practices

Fluent in at least one programming language such as: Python, Java/Spring Boot, .Net

Demonstrated experience using enterprise-authorized AI capabilities within the work environment to improve SRE workflows (e.g., incident investigation support and knowledge capture) with strong validation habits and awareness of data sensitivity.

Ability to evaluate AI-assisted operational recommendations for correctness and risk, define appropriate guardrails for team usage, and ensure outcomes align to resiliency and security expectations.

Proficient knowledge and experience in observability such as white and black box monitoring, service level objective alerting, and telemetry collection

Proficient with continuous integration and continuous delivery practices and tooling

Proficient with container and container orchestration

Experience with troubleshooting common networking technologies and issues

Advanced knowledge of software applications and technical processes with emerging depth in one or more technical disciplines, and actively self-educates to evaluate and recommend suitable new technologies