Lead Software Engineer - Reliability Support

What you'll do

• Designs, develops, tests, and troubleshoots software and complex applications.
• Uses data and monitoring to improve reliability, identify bottlenecks, and maintain dashboards, reports, and alerts.
• Leads AI-assisted reliability workflows and environment-management automation, with attention to validation, traceability, and security.

What they ask for

• Formal training or certification in software engineering concepts and proficient applied experience.
• Hands-on experience in system design, application development, testing, and operational stability; experience developing, debugging, and maintaining code in a large corporate environment.
• Proficiency in Java and/or Python; experience with large-scale technology environments, including on-premises and public cloud, such as Kubernetes and Amazon Web Services.
• Experience with monitoring tools such as Geneos, Dynatrace, or Datadog; developing Splunk dashboards, reports, and alerts; ticketing systems such as ServiceNow and Jira Service Desk; and knowledge of SDLC, CI/CD, resiliency, and security.

Nice to have

• Experience building reliability automation, automated remediation, self-healing patterns, runbook automation, or observability improvements.
• Experience designing governance for AI-assisted engineering, or mentoring engineers and leading initiatives across teams.

Tools

Java, Python, Kubernetes, Amazon Web Services, Geneos, Dynatrace, Datadog, Splunk

AI focus

Leads use of approved AI-assisted development tools and applies Agentic AI to automate and augment environment management, including incident detection and remediation, root-cause analysis, predictive alerting, self-healing infrastructure, runbooks, and observability. Promotes reusable AI-assisted reliability workflows with validation, traceability, auditability, and security controls.