Job responsibilities

• Demonstrates and champions site reliability culture and practices and exerts technical influence throughout your team
• Leads initiatives to improve the reliability and stability of your teams applications and platforms using data-driven analytics to improve service levels
• Collaborates with team members to identify comprehensive service level indicators and stakeholders to establish reasonable service level objectives and error budgets with customers
• Demonstrates a high level of technical expertise within one or more technical domains and proactively identifies and solves technology-related bottlenecks in your areas of expertise
• Acts as the main point of contact during major incidents for your application and demonstrates the skills to identify and solve issues quickly to avoid financial losses
• Documents and shares knowledge within your organization via internal forums and communities of practice
• 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.
• 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.

Required qualifications, capabilities, and skills

• Bachelors Degree in Computer Science, Cybersecurity, Data Science, or related disciplines
• Formal training or certification on site reliability engineering concepts and 5+ years applied experience
• Deep proficiency in reliability, scalability, performance, security, enterprise system architecture, toil reduction, and other site reliability best practices with the ability to implement these practices within an application or platform
• Fluency in at least one programming language such as (e.g., Python, Java Spring Boot, .Net, etc.) and possesses deep knowledge of software applications and technical processes with emerging depth in one or more technical disciplines
• Proficiency and experience in observability such as white and black box monitoring, SLO alerting, and telemetry collection using tools such as Grafana, Dynatrace, Prometheus, Datadog, Splunk, etc.
• Proficiency in continuous integration and continuous delivery tools (e.g., Jenkins, GitLab, Terraform, etc.) and experience with container and container orchestration (e.g., ECS, Kubernetes, Docker, etc.)
• Experience with troubleshooting common networking technologies and issues
• Ability to identify and solve problems related to complex data structures and algorithms
• Demonstrates a strong commitment to continuous learning and emerging technologies, with the ability to mentor team members on new programming languages while effectively collaborating and influencing stakeholders across all levels of the organization.
• 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.