The Role
We are seeking an AI Applied Engineer with strong backend software engineering experience to help maintain the health of our production systems by resolving complex escalations and implementing automation. Our core backend relies heavily on Java, Spring Boot, and microservices orchestrated via Kubernetes.
In this role, we embrace a T-shaped engineering profile. You will anchor your work in your strong knowledge of the Java ecosystem, while utilizing AI tools to operate across the wider tech stack. Because system stability is a 24/7 priority, our team utilizes a "follow the sun" model, meaning you will participate in an on-call rotation and handle critical incidents outside of standard business hours.
Rather than just closing tickets, you will use AI and agentic workflows to optimize our support processes, automate repetitive triage tasks, and implement permanent fixes for underlying system defects. You will be a key contributor who collaborates with senior engineers to raise the bar on how we handle application support in a modern, cloud-native environment.
Key Responsibilities
End-to-End Troubleshooting: Investigate and resolve complex escalations by debugging Java and Spring Boot code, tracing issues through REST APIs, and identifying bottlenecks in our microservices architecture.
Global Incident Response: Participate in our "follow the sun" support model, which includes an on-call rotation to acknowledge alerts and troubleshoot high-priority incidents outside of standard business hours.
AI-Driven Automation: Implement agentic tooling, LLMs, and AI automation to streamline incident triage, log analysis, and repetitive support workflows as you identify system pain points.
Infrastructure Navigation: Monitor, debug, and support application stability within a Kubernetes-orchestrated containerized environment.
Technical Collaboration: Work closely with senior engineers, core development, and product teams to escalate critical bugs, test patches, and share knowledge across the team.
Proactive Problem Solving: Help identify trends in system failures, participate in cross-system investigations, and contribute to code refactoring or automation initiatives to prevent recurrence.
