5+ years of professional software engineering, with at least 2 years focused on applied AI in production systems.

Proficient in Python and/or Go; comfortable reading and writing in the other.

Proven experience building and scaling multi-agent or agent-driven systems in production real-world operational ownership, not just simple LLM workflows.

Hands-on experience with modern agent ecosystems, including frameworks (e.g., LangGraph, Google ADK, Mastra, Claude Agent SDK), observability and evals tooling (e.g., Langfuse, LangSmith, Braintrust), MCP implementations, and leading AI SDKs (e.g., OpenAI, Anthropic).

Strong systems and backend architecture fundamentals designing scalable, reliable systems and handling infrastructure, performance, failure modes, cost, and deployment concerns.

Good understanding of cloud-native environments (GCP and/or AWS) compute, storage, networking, and managed AI services.

Experienc...