Senior AI Systems Engineer — Agentic Platforms
Senior AI Systems Engineer — Agentic Platforms
The Moment
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 how the platform evolves.
The Role
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.
What You’ll Build
• 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
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. You pair that velocity with discipline: guardrails, verification, and architectural boundaries that keep systems safe as autonomy increases.
What We’re Looking For
We care far more about what you’ve built than what’s on your resume.
You:
• 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
• 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 autonomy and take systems end-to-end
Technical requirements:
• 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
Culture
Small team, high autonomy, high ownership. We move fast, prototype aggressively, and ship what works. We maintain high standards around reliability, security, and clarity. We value builders, explorers, and inventors who want to help define the future of agentic systems.
Compensation and Location
Compensation: $170,000–$220,000 base salary plus competitive equity
Location: Venice, San Francisco, Remote, or Hybrid
How to Apply
Send us:
• 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.
Apply tot his job
Apply To this Job
The Moment
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 how the platform evolves.
The Role
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.
What You’ll Build
• 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
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. You pair that velocity with discipline: guardrails, verification, and architectural boundaries that keep systems safe as autonomy increases.
What We’re Looking For
We care far more about what you’ve built than what’s on your resume.
You:
• 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
• 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 autonomy and take systems end-to-end
Technical requirements:
• 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
Culture
Small team, high autonomy, high ownership. We move fast, prototype aggressively, and ship what works. We maintain high standards around reliability, security, and clarity. We value builders, explorers, and inventors who want to help define the future of agentic systems.
Compensation and Location
Compensation: $170,000–$220,000 base salary plus competitive equity
Location: Venice, San Francisco, Remote, or Hybrid
How to Apply
Send us:
• 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.
Apply tot his job
Apply To this Job