AI Security - AI Platform Team Lead

3 weeks, 5 days ago
Lead
Artificial Intelligence and Machine Learning
Cato Networks

Cato Networks

Cato Networks is the world's leading single vendor SASE platform that converges SD WAN, security, global backbone, and remote access into a global cloud-native service. Their robust platform optimizes and secures application access for all users and lo...

Diversified Telecommunication Services
251-1K
Founded 2015
$770M raised

Description

  • Build and lead the AI Platform team, including hiring, mentoring, architecture, technical direction, and execution.
  • Own the AI security runtime platform for high-throughput, low-latency inline security decisions across the global cloud and PoPs.
  • Design the orchestration layer for running GPU models, CPU heuristics, and security logic as one production engine.
  • Own production readiness, including observability, SLOs, autoscaling, reliability, rollout, rollback, and operational health.
  • Own the model lifecycle platform, including registry, versioning, deployment, monitoring, and safe production rollout.
  • Work closely with research and algorithm teams to productionize AI security models and algorithms at scale.
  • Define the long-term platform strategy for AI runtime and model serving at Cato.

Requirements

  • 3+ years of leadership experience as a team lead, tech lead, or engineering manager.
  • 3+ years of hands-on experience in AI inference, production ML infrastructure, model serving, or AI runtime platforms.
  • Strong experience with production inference technologies such as Triton, vLLM, CUDA, Kubernetes, Docker, PyTorch, ONNX, TensorRT, or similar.
  • 3+ years of experience with Go, or strong experience with a similar high-performance backend language such as C++, Rust, or Java.
  • Experience with performance optimization, scalability, observability, and SLO-driven production ownership.
  • Strong system design skills, especially around distributed systems, performance, reliability, and production infrastructure.
  • Experience with GPU optimization, GPU scheduling, GPU resource efficiency, quantization, runtime acceleration, or large-scale model serving.

Interested in this position?

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