Senior AI Systems Engineer (Agentic Platforms)

• 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
• 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 how the platform evolves
• You will design, build, and operate core systems that enable autonomous agents to function reliably in production
• This is applied systems engineering with AI at the center, not ML research and not chatbot wrappers
• You’ll build production-grade agentic workflows, retrieval and memory systems, multi-model execution, and tool-calling integrations that interact safely with enterprise systems
• This is also frontier work
• Many of the patterns for agentic systems are still emerging
• You’ll explore new approaches, prototype quickly, and turn what works into durable production systems
• At the same time, strong distributed systems fundamentals still apply
• These systems must be reliable, secure, observable, debuggable, and maintainable under real-world conditions
• Agent execution systems, including autonomous task loops, scheduling, triggers, and control planes
• Retrieval and memory architectures, including context management, long-term memory, and structured memory
• 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, failure isolation, and recovery paths
• 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
• You pair that velocity with discipline: guardrails, verification, and architectural boundaries that keep systems safe as autonomy increases

Benefits

• Unlimited vacation and paid time off policies
• Comprehensive health, dental, and vision insurance
• Equity package- Have built and operated complex backend or distributed systems in production
• Have built LLM-powered or AI-native systems beyond demos, with real users and real constraints
• Have strong judgment around reliability, security, observability, and failure modes
• Operate with high ownership and autonomy and take systems end-to-end
• Use AI as a core part of your development workflow, not as an occasional convenience
• Are comfortable operating in ambiguous frontier areas and validating ideas through rapid iteration
• Familiarity with enterprise security patterns is a plus
• TypeScript required, Python strongly preferred
• Domain familiarity with DevOps, SecOps, or infrastructure automation is a plus
• Experience with production infrastructure; Docker/Kubernetes experience is a plus
• Strong SQL proficiency- Your work. A portfolio, demo, GitHub profile, blog post, recorded walkthrough, or any evidence of AI systems you’ve built. We want to see how you think, not just what you’ve done
• A brief note on what excites you about agentic systems and where you think the hard problems are
• Your resume, but know that the first two items matter more
• We’ll move fast. The interview process is designed to see how you actually work, not quiz you on trivia