Strong backend engineering background: designing APIs, services, queues, storage, integrations, and production infrastructure
Proven ownership of complex systems end-to-end — from choosing an approach and breaking it down, to shipping to production and iterating afterward
Hands-on experience with LLM/AI systems: RAG, tooling, agents, evaluation, model limitations, cost, quality, and reliability of AI-driven scenarios
Solid grasp of platform constraints: SSO, roles and permissions, data isolation, auditing, secrets management, security, and maintainability
Willingness to write code yourself, build key platform components, and review your teammates' work
Track record in technical leadership: setting engineering standards, breaking down tasks, and mentoring engineers
A pragmatic mindset: spotting the MVP, testing hypotheses fast, managing technical debt, and moving iteratively
Ability to explain complex trade-offs in plain language and help teams make hard calls under uncertainty