You will define, build, and evolve foundational systems that enable autonomous agents to operate reliably in production.
This is applied systems engineering with AI at the center, not ML research and not chatbot wrappers.
You’ll work on agent execution frameworks, retrieval and memory systems, multi-model execution, and secure tool-calling integrations that interact with real enterprise environments.
This role also requires invention. Many of the patterns for agentic systems are still emerging. You’ll explore new approaches, prototype quickly, and turn what works into durable platform foundations.
You’ll identify high-leverage architectural improvements, abstractions, and guardrails that expand what the platform can do while keeping it reliable, secure, observable, and maintainable under real-world conditions.
Staff engineers at Kindo are builders and inventors with strong architectural judgment. You help define both what we build next and which approaches become the system’s durable defaults.
Requirements
Have deep experience building and operating complex backend or distributed systems in production
Have built LLM-powered or AI-native systems beyond demos, with real users, real constraints, and real failure modes
Have strong architectural judgment around reliability, security, observability, and system evolution
Have invented or introduced abstractions, workflows, or architectural approaches that materially improved system capability or engineering effectiveness
Are comfortable operating in ambiguous frontier areas and validating ideas through rapid iteration
Use AI as a core part of your development workflow, not as an occasional convenience
Operate with high ownership and take systems end-to-end, including long-term evolution.
TypeScript required, Python strongly preferred
Strong SQL proficiency
Experience with production infrastructure; Docker/Kubernetes experience is a plus
Familiarity with enterprise security patterns is a plus
Domain familiarity with DevOps, SecOps, or infrastructure automation is a plus.