Principal AI Systems Engineer — Agentic Platforms

About the position The role of the software engineer is changing. Autonomous agents can now execute real workflows, operate infrastructure, and improve over time. The hard problems are shifting from model demos to production systems: orchestration, memory, reliability, control, and security. OpenAI acquired OpenClaw. Meta paid $2B for Manus. The agent platform layer is becoming one of the most important layers in the stack. At Kindo, we’re already there. Our platform runs autonomous agents in production at real enterprises, automating DevOps and SecOps workflows with real permissions, real consequences, and real reliability requirements. About Kindo Kindo is an agent automation platform for DevOps and SecOps teams. We help organizations automate high-friction operational work using autonomous agents that run reliably, securely, and at scale. Our platform supports deployment on-prem, in hybrid environments, or in the cloud, with enterprise-grade security controls from day one. We’re a small, highly technical team with strong customer traction and real enterprise revenue. Engineers have direct ownership over critical systems and shape the platform’s technical direction and long-term evolution. The Role You will define and evolve the architectural foundations of Kindo’s agent platform. This is applied systems engineering at the frontier of AI-native development, not ML research and not chatbot wrappers. You’ll work on agent execution frameworks, memory architectures, multi-model execution, secure tool-calling integrations, and the platform primitives that determine what autonomous systems can reliably do. This role requires invention and unusually strong technical judgment. Many of the paradigms for agentic systems are still emerging. You’ll continuously track the ecosystem, explore new approaches, prototype quickly, and decide what becomes the platform’s durable foundation. You will identify the highest-leverage architectural opportunities, the failure modes most likely to bite us, and the guardrails and abstractions that let the system scale safely. Principal engineers at Kindo are builders and inventors who help determine what the future of agentic systems should look like, while ensuring the platform remains reliable, secure, observable, debuggable, and maintainable under real-world conditions. How You Build AI is a first-class tool in how we engineer. You use AI across design, prototyping, implementation, testing, debugging, and incident response, and you continuously refine workflows that increase leverage without sacrificing quality. At this level, you also help define the engineering paradigm itself: how we use autonomous agents and AI-driven workflows to compound velocity over time while minimizing slop, security risk, and architectural drift. You build with discipline. You create boundaries, verification strategies, and operational guardrails so systems remain understandable and controllable as autonomy increases. You ensure the platform can evolve rapidly without losing reliability and security. What We’re Looking For We care far more about what you’ve built than what’s on your resume.

Responsibilities

• Agent execution architectures, including autonomous task loops, scheduling, triggers, control planes, and failure isolation
• Retrieval and memory architectures, including persistent, structured, and contextual memory systems
• Multi-model routing and orchestration across providers, balancing quality, latency, cost, and failure modes
• Tool-calling and integration frameworks for safe interaction with external services and enterprise environments
• Reliability, security, and operability foundations, including evaluation, observability, auditing, and recovery paths
• Core platform primitives and architectural patterns that enable entirely new autonomous capabilities safely

Requirements

• Have deep expertise designing and operating complex backend or distributed systems in production
• Have built and evolved platform-level architectures that remained durable under rapid change
• Have built LLM-powered or AI-native systems beyond demos, with real users, constraints, and failure modes
• Demonstrate exceptional architectural judgment around reliability, security, observability, and long-term system evolution
• Have 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
• TypeScript required, Python strongly preferred
• Strong SQL proficiency

Nice-to-haves

• 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

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