Kindo

Principal AI Systems Engineer – Agentic Platforms

Kindo

full-time

Posted on:

Location Type: Hybrid

Location: VeniceCaliforniaUnited States

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Salary

💰 $250,000 - $330,000 per year

Job Level

About the role

  • Define and evolve the architectural foundations of Kindo’s agent platform
  • Work on agent execution frameworks, memory architectures, multi-model execution, secure tool-calling integrations, and platform primitives
  • Identify highest-leverage architectural opportunities, failure modes, guardrails, and abstractions for safe scaling
  • Help determine what the future of agentic systems should look like while ensuring reliability, security, observability, debuggability, and maintainability under real-world conditions

Requirements

  • Deep expertise designing and operating complex backend or distributed systems in production
  • Built and evolved platform-level architectures that remained durable under rapid change
  • Built LLM-powered or AI-native systems beyond demos, with real users, constraints, and failure modes
  • Exceptional architectural judgment around reliability, security, observability, and long-term system evolution
  • Invented or introduced foundational abstractions, workflows, or architectural approaches that materially improved system capability or engineering effectiveness
  • Actively track emerging tools, models, and approaches and translate the best of them into production systems
  • Use AI as a core part of your engineering workflow, not as an occasional convenience
  • Operate with exceptional ownership and take systems end-to-end, including long-term evolution.
Benefits
  • Competitive equity
Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools
backend systemsdistributed systemsplatform-level architectureLLM-powered systemsAI-native systemsarchitectural judgmentreliability engineeringsecurity engineeringobservabilitydebuggability
Soft Skills
exceptional ownershiparchitectural judgmentsystem evolutionengineering effectiveness