Kubernetes Security Engineer

Built multi-step agentic workflows with tool use and function calling Experience with agent orchestration frameworks (LangGraph, CrewAI, or custom) Built guardrails, fallbacks, or graceful degradation for AI systems Streaming inference and async agent orchestration Cost/latency optimization: caching, batching, prompt compression ML observability tools: Langfuse, Arize, Braintrust, W&B Retrieval systems (vector search, hybrid search) as a tool, not the focus Screening Questions for Candidates

• "Describe a production AI agent or skill system you built. What broke and how did you fix it?"

• "Have you built MCP servers/integrations or custom tool-use systems for LLMs?"

• "How do you evaluate whether an LLM-based feature is working well? What makes this hard?"

• "Walk me through how you'd deploy and scale an AI service on Kubernetes."

Not a Fit

If Primarily a model trainer/fine-tuner (we're not training models) AI experience is mainly academic, research, or tutorial-based No production systems experience (only notebooks/demos) Looking for entry-level role with heavy mentorship Background is primarily data science/analytics rather than engineering "Architects" who don't write or deploy code themselves

For applications and inquiries, contact: hirings@openkyber.com

Do you have experience in Scalable systems?, Requirement Details Backend/Systems Experience 3+ years building production backend or distributed systems (pre-AI experience required) Production AI Systems Has shipped AI/LLM features serving real users at scale not just prototypes or demos Agentic Systems Has built AI agents, skills, tools, or MCP (Model Context Protocol) integrations Python Proficient for backend development Secondary Language Working knowledge of Go, TypeScript, or Rust Cloud Infrastructure Deep experience with AWS/Google Cloud Platform/Azure cost optimization, compute decisions, not just deployment Container & Orchestration Hands-on with Docker and Kubernetes can build, deploy, debug, and scale services themselves LLM Integration Understands token economics, context limits, rate limiting, structured outputs, API failure modes LLM Evaluation Understands how to evaluate LLM outputs and the inherent challenges (non-determinism, quality measurement, regression detection) Hands-On Engineer Not just an architect writes code, debugs production issues, deploys their own work Preferred / Differentiators