Site Reliability Engineer

Job responsibilities

• Executes small to medium projects independently with initial direction and eventually graduates to designing and delivering projects by yourself
• Leverages technology to solve business problems by writing high quality, maintainable, and robust code following best practices in software engineering
• Participates in triaging, examining, diagnosing, and resolving incidents and work with others to solve problems at their root
• Recognizes the toil within your role and proactively works towards eliminating it through either systems engineering or updating application code
• Understands observability patterns and strives to implement and improve service level indicators, objectives monitoring, and alerting solutions for optimal transparency and analysis
• Uses enterprise-authorized AI capabilities within the work environment to speed up incident triage, troubleshooting, and post-incident analysis, validating outputs and handling operational data according to sensitivity and security requirements
• Applies enterprise-authorized AI capabilities within the work environment to identify recurring toil and reliability risks from operational signals, prioritizing reuse-first improvements and measurable SLO outcomes

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 2+ years applied experience
• Proficient in site reliability culture and principles and familiarity with how to implement site reliability within an application or platform, including strong understanding of SLI/SLO/SLA and error budgets
• Experience in observability such as white and black box monitoring, service level objective alerting, and telemetry collection using tools such as Grafana, Dynatrace, Geneos, Prometheus, Datadog, Splunk, and others
• Proficient knowledge of software applications and technical processes within a given technical discipline (e.g., Cloud, AI, Android, etc.), including hands-on experience in system design, resiliency, testing, operational stability, and disaster recovery
• Strong expertise in AWS services, including EC2, S3, RDS, VPC, IAM, and networking.
• Proficient in at least one programming language such as Python, Java/Spring Boot for AI/ML modeling and automation to reduce operational toil by building tools for repeated tasks
• Experience with continuous integration and continuous delivery tooling, along with experience running production incident calls and managing incident resolution in collaboration with cross-functional teams
• Experience with continuous integration and continuous delivery tools like Jenkins, GitLab, or Terraform
• Practical cloud experience in AWS supporting production services (visibility/troubleshooting, deployments, and operational hygiene) and strong debugging fundamentals across distributed systems (logs/metrics, latency analysis, dependency failures, data issues)
• Working knowledge of using enterprise-authorized AI capabilities within the work environment to support SRE workflows (e.g., troubleshooting support and runbook drafting) with strong validation habits and awareness of data sensitivity.  Ability to assess AI-assisted operational recommendations for correctness and risk, and apply appropriate controls to maintain resiliency, security, and auditability

Preferred qualifications, capabilities, and skills

• Experience with continuous integration and continuous delivery tooling
• Experience applying AI-assisted tooling to reduce support toil
• Familiarity with container and container orchestration and troubleshooting common networking technologies and issues
• Ability to identify new technologies and relevant solutions to ensure design constraints are met by the software team
• Ability to initiate and implement ideas to solve business problems