Sr Staff IT Software Engineer AI

Palo Alto Networks•Office - USA - CA - Headquarters, CA

1d•$145,000 - $235,500•Onsite

About The Position

At Palo Alto Networks, we are united by a shared mission to protect our digital way of life. We thrive at the intersection of innovation and impact, solving real-world problems with cutting-edge technology and bold thinking. In order to be the cybersecurity partner of choice, we must trailblaze the path and shape the future of our industry. This is something our employees work at each day and is defined by our values: Disruption, Collaboration, Execution, Integrity, and Inclusion. We weave AI into the fabric of everything we do and use it to augment the impact every individual can have. We believe collaboration thrives in person. That’s why most of our teams work from the office full time, with flexibility when it’s needed. This model supports real-time problem-solving, stronger relationships, and the kind of precision that drives great outcomes.

Requirements

Strong software engineering fundamentals in TypeScript (Node.js), Go, or Python.
Experience interfacing with LLM APIs (OpenAI, Anthropic, Google Vertex AI, AWS Bedrock), vector databases, and prompt engineering for systems-level orchestration.
Deep proficiency in at least two major cloud platforms (AWS, Azure, GCP) with a strong architectural understanding of the third.
Expert-level knowledge of Kubernetes (CKA preferred) and cloud-native networking.
Experience building intelligent delivery pipelines using GitHub Actions or GitLab CI, featuring integrated automated testing, security gates, and AI-assisted code reviews.
Deep understanding of Linux internals, distributed systems architecture, asynchronous programming patterns, and performance tuning.
6+ years of experience in Cloud Software Engineering, Site Reliability Engineering (SRE), or Distributed Systems Infrastructure with a BS or related Bachelor’s degree.
2+ years of hands-on experience integrating AI tools, LLMs, or predictive analytics into deployment workflows, pipelines, or software platforms.
Proven track record of architecting and operating large-scale, high-throughput distributed systems.
A mindset that moves past "how do I automate this task?" to "how do I build an autonomous system that solves this permanently?"
Ability to partner with Core AI/ML teams to bridge the gap between model deployment and high-availability cloud infrastructure.

Responsibilities

Architect and maintain scalable cloud systems across AWS, Azure, and GCP using Pulumi, AWS CDK, or Terraform. Integrate AI development workflows and custom LLM agents to accelerate safe infrastructure compilation, drift detection, and automated cross-cloud refactoring.
Engineer custom software utilities, internal services, and autonomous agents using (TypeScript/Node.js, Go, or Python, alongside frameworks like LangChain or CrewAI) to orchestrate complex provisioning, predictive auto-scaling, and closed-loop self-healing systems.
Leverage predictive machine learning models to analyze multi-cloud spend patterns, autonomously executing real-time resource-optimization strategies via API-driven software actions (e.g., dynamic spot-instance bidding, intelligent right-sizing across AWS, Azure, and GCP).
Implement next-gen observability frameworks (OpenTelemetry, Prometheus) coupled with AI anomaly detection. Embed security directly into the deployment pipeline, utilizing LLMs to automatically audit Cloud IAM policies, scan for vulnerabilities, and generate contextual patches.
Manage production-grade Kubernetes clusters (EKS, AKS, GKE). Optimize resource allocation, cluster auto-scaling, and service meshes using AI-driven traffic routing and predictive capacity planning.
Act as a tier-3 software escalation engineer for complex distributed systems anomalies. Help design and train our internal "On-Call AI Agent" to ingest logs, perform automated Root Cause Analysis (RCA), and submit pre-validated Pull Requests to resolve underlying system defects.