IT Principal AI Engineer

Palo Alto Networks•Santa Clara, CA

1d•$167,000 - $270,500•Onsite

About The Position

Your Career You will join a dynamic and fast-paced team of seasoned fellow engineers designing and developing multi-tiered applications in a rapidly growing company. As a Principal AI Engineer, you will leverage your extensive experience to act as the trailblazer, helping us build and innovate enterprise-grade full-stack systems with a specific focus on Generative AI transformation. We are looking for a highly hands-on, extremely technical leader to architect the next generation of our internal and external business applications. You will move beyond traditional software engineering to design intelligent, agentic workflows that revolutionize our Go-To-Market (GTM) processes.

Requirements

10+ years of overall IT system architecture, design, development, and deployment experience.
Proven track record leading technical teams in Agile/Scrum environments for large-scale implementations with aggressive timelines.
Experience partnering with business stakeholders to design workflows for Go-To-Market (GTM), sales, or support operations.
Bachelor’s degree or equivalent in Computer Science or a related field.
Strong verbal and written communication skills with the ability to explain complex AI concepts to senior leadership.
Extensive knowledge in building modern applications using Large Language Models (LLMs) such as Llama 3, GPT-4, and Anthropic.
Deep experience building and maintaining highly observable GenAI systems, including proficiency with tracing and evaluation tools (e.g., LangSmith, Arize, HoneyHive, or custom OpenTelemetry).
Deep experience with Multi-Agent systems, Agent-to-Agent (A2A) communication, and orchestration frameworks.
Experience establishing "Golden Datasets" for ground truth testing and performing comparative analysis across different models.
Expert-level proficiency in Python; familiarity with Java or Node.js for enterprise integration is a plus.

Responsibilities

Lead the architecture and implementation of a Proxy-First "AI Gateway" intermediary layer to ensure seamless LLM vendor independence.
Design and build a unified API endpoint to abstract LLM complexities and provide centralized control for access and security.
Implement comprehensive GenAI observability pipelines to track trace-level data, prompt inputs/outputs, and model latency.
Create architecture to optimize performance and expense by intelligently routing simpler queries to cost-effective models.
Integrate observability data into the development cycle to identify real-time bottlenecks, high-latency chains, and model drift.
Institute and promote innovative thinking by incorporating Agentic AI, designing sophisticated Multi-Agent Systems and Agent-to-Agent (A2A) workflows.
Apply GenAI to complex Go-To-Market (GTM) business logic, automating critical workflows like customer support and entitlement platforms.
Pioneer "LLM as a Judge" testing methodologies to automate quality assurance of system outputs (correctness, tone, and helpfulness).
Build robust guardrails to filter inputs and outputs, preventing PII exposure, offensive content, and prompt injection attacks.
Develop detection mechanisms, including keyword filtering, behavioral analysis, and adversarial training, to protect model instructions from manipulation.