Senior AI Field Engineer: Edge, Multi-Cloud Deployments

Palo Alto Networks; New York (NY); On-site; USD 147,000 - 238,000; Full time

Job summary

Palo Alto Networks is seeking a Forward Deployed Engineer for Prisma AIRS to embed with customer teams, write production code, and design low-latency topologies. You will build custom plugins and bridge customer environments with the core platform, delivering secure AI workloads to production.

You will lead architecture discussions, deploy in hybrid/multi-cloud setups, and work closely with Product and Core SWE to turn patterns into platform features.

Qualifications

• Bachelor's or Master's degree in Computer Science, AI, ML, or related field, or 7+ years in software engineering.
• Experience building production-grade APIs and integrating enterprise APIs (REST, gRPC, WebSockets).
• Hands-on with LLM orchestration, prompt security, token optimization, and vector DBs/RAG.

Responsibilities

• Write production-grade code to integrate Prisma AIRS with microservices and multi-clouds.
• Develop middleware/plugins for identity-aware policies, prompt security, and telemetry pipelines.
• Build deployment modules (Terraform, Helm, Kubernetes) and SDK wrappers.
• Optimize high-throughput, low-latency traffic paths and load balancing.
• Lead ADRs with CISOs for zero-trust deployments and architecture reviews.
• Deploy gateway topologies across multi-LLM providers and handle live production issues.
• Analyze traces to diagnose edge-case AI framework failures and upstream plugins.

Skills

Python

Go

TypeScript

C++

API Development

LLM Orchestration

Cloud Native

Kubernetes

Docker

Terraform

OpenTelemetry

Prometheus

Zero Trust

Education

Bachelor's or Master's in CS/AI or related field

Tools

Envoy

Kong

Terraform

OpenTelemetry

Job description

Palo Alto Networks is seeking a Forward Deployed Engineer for Prisma AIRS to embed with customer teams, write production code, and design low-latency topologies. You will build custom plugins and bridge customer environments with the core platform, delivering secure AI workloads to production.

You will lead architecture discussions, deploy in hybrid/multi-cloud setups, and work closely with Product and Core SWE to turn patterns into platform features.