Lead Software Engineer - SRE

• Design, code, test, and deliver software to automate manual operational work
• Troubleshoot priority incidents, facilitate blameless post-mortems and ensure permanent closure of incidents
• Engage with development team throughout the life cycle to help develop software for reliability and scale, ensuring minimal refactoring or changes
• Identify application patterns and analytics in support of better service level objectives
• Design self-healing and resiliency patterns
• Design automated software and product upgrades, change management, and release management solutions
• Coach or manage teams as applicable
• Participate in the 24x7 support coverage as needed
• Leverage AI-powered tools and platforms to enhance observability, incident response, and operational efficiency
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.

• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience 
• Exposure to tools related to CI/CD, Application Resiliency, and Security 
• Exposure to microservice architecture, AWS and Containers
• Exposure to AI tools (copilot, claude code,LLM)and their application in SRE workflows for faster delivery and smarter operations
• Emerging knowledge of software applications and technical processes within a technical discipline, specifically building out cloud native solutions (AWS).
• Working knowledge of infrastructure components (e.g. routers, load balancers, cloud products, container systems, compute, storage, and networks)
• Excellent debugging and trouble shooting skills
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
  • Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices