Staff/Sr. Staff AI Engineer - IT Business Applications

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

2d•Onsite

About The Position

We are hiring AI Engineers at the Staff and Senior Staff levels to join our IT Business Applications team. This group builds and operates the technology platforms that power Marketing, Sales, and the full Lead-to-Cash lifecycle. This is a solutions engineering role, not a data science or ML research role. You will act as a hands-on technical leader who uses AI as building blocks—LLMs, agents, RAG, embeddings—to deliver production solutions that solve real business problems. You will partner directly with business and product stakeholders, translate requirements into scalable architecture, and own delivery end-to-end from design through production operations. You will move beyond traditional software engineering to design intelligent, agentic workflows that revolutionize our Go-To-Market (GTM) and customer experience processes—ensuring our platforms are the most secure and efficient in the industry, directly improving operational revenue generation.

Requirements

Staff: 5+ years of experience (or 8+ with a Master's degree).
Sr. Staff: 8+ years of experience (or 6+ with a Master's, 3+ with a PhD).
Expert-level proficiency in Python and strong proficiency in Go, Java, or Node.js.
Deep experience with microservices, distributed systems, and cloud platforms (AWS/Azure/GCP).
Hands-on experience building production apps with modern LLMs (GPT-4, Llama 3, Anthropic Claude) and orchestration frameworks (LangChain, LangGraph).
Deep understanding of RESTful API design, event-driven architectures, CI/CD, and Agile/Scrum delivery.
Bachelor's degree in Computer Science or equivalent experience.

Nice To Haves

AI tracing tools (LangSmith, Arize, HoneyHive)

Responsibilities

Translate business requirements into scalable, modern technical architectures meeting non-functional requirements (performance, reliability, scalability, security, observability).
Contribute to building Proxy-First AI Ecosystems and unified API endpoints to abstract LLM complexity and enable vendor independence.
Design and build Multi-Agent Systems and Agent-to-Agent (A2A) workflows capable of planning, multi-step execution, and self-correction.
Apply GenAI to automate GTM logic across customer support, sales compensation, and entitlement platforms.
Implement observability pipelines tracking trace-level data, prompt inputs/outputs, model latency, and cost.
Deploy "LLM as a Judge" evaluation frameworks and integrate real-time feedback loops to fix bottlenecks and model drift.
Build robust input/output guardrails to prevent PII exposure and prompt injection attacks.
Implement graceful degradation strategies so systems remain resilient during component failures.
Drive features and subsystems from prototype to production.
Mentor engineers, uphold engineering standards, and clearly communicate technical trade-offs to business partners.