
Principal AI Systems Engineer – Agentic Platforms
Kindo
full-time
Posted on:
Location Type: Hybrid
Location: Venice • California • United States
Visit company websiteExplore more
Salary
💰 $250,000 - $330,000 per year
Job Level
Tech Stack
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