QA Automation Engineer

• Lead the design and development of production AI systems powering insurance workflow automation.
• Architect AI orchestration layers connecting LLMs, backend services, and business workflows.
• Own end-to-end AI system design, including inference pipelines, routing, caching, and fallback strategies.
• Drive engineering decisions around latency, reliability, cost, and scalability of AI services.
• Lead implementation of observability systems (logging, monitoring, tracing, alerting).
• Review and guide backend AI implementation across engineering teams.
• Collaborate with product, backend, DevOps, and operations teams to ship end-to-end AI features.
• Debug complex production issues across distributed AI systems and lead root-cause analysis.
• Define engineering standards and best practices for AI system development.
• Mentor engineers and elevate technical execution quality across teams.

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Lead AI Engineer – AI Systems, Automation