Principal AI Systems Engineer, Agentic and Productivity Systems

What you'll do

• Design and prototype agent-based systems for CI diagnostics, code review automation, and autonomous debugging.
• Develop multi-agent orchestration patterns with structured state, memory, and control boundaries.
• Build reusable orchestration layers and service architectures for AI-powered engineering systems.
• Develop structured evaluation pipelines including trace-based evaluation and regression testing for agent behavior.
• Convert experimental AI workflows into secure, scalable, and production-grade services.
• Implement observability, tracing, cost controls, and model routing for AI systems.
• Define internal standards for AI experimentation, evaluation, deployment, and monitoring.
• Build cohesive infrastructure to prevent tool sprawl and enable reusable AI productivity systems.

What we're looking for

• 8+ years of software engineering experience with demonstrated depth in systems-level work.
• Strong systems engineering experience using Python, Go, TypeScript, or similar languages.
• Experience building distributed systems, developer platforms, or infrastructure services.
• Experience integrating LLMs or AI APIs into production systems.
• Experience evaluating and integrating multiple AI providers including cost optimization and capacity planning.
• Strong understanding of observability, metrics, logging, and tracing systems.
• Experience operating production services at scale.
• Experience with agent frameworks, embeddings, vector databases, RAG architectures, or CI/CD platforms (preferred).