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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI-First Core IT Software Engineering: Software, ML & Data (Staff - Principal) - **Company:** Palo Alto Networks - **Location:** Santa Clara, CA, United States - **Experience:** Expert - **Salary:** $145,000.0 - $235,500.0 - **Contract:** Permanent contract - **Skills:** Java (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Business Logic, Automated Storage and Retrieval Systems, Microsoft Azure, Cloud Computing, Continuous Integration, Digital Architecture, Distributed Systems, Fault Tolerance, Graph Database, Python (Programming Language), Machine Learning, Node.Js, Performance Tuning, Scrum Methodology, Software Safety, Software Engineering, Epic Haiku, Large Language Models, Multi-Agent Systems, IT Architecture, Event Driven Architecture, Build Management, Information Technology, OPUS (Software), Virtual Agents, Api Design, GPT, Golang, Microservices - **Published:** July 2, 2026 - **Apply:** https://dejobs.org/x/x/32C53798069544839C4314C4C04735EC/job/ ## About the Role Principal - 12+ years (or 8+ with Master's) Senior Principal - 15+ years (or 12+ with Master's, 8+ with PhD) Experience All Levels: * Expert-level proficiency in Python; strong proficiency in at least one additional core language (Go, Java, Node.js, or equivalent). * Proven, hands-on experience building applications using Large Language Models (LLMs) such as GPT-4, Llama 3, and Anthropic Claude (Haiku, Sonnet, Opus, Fable). * Deep experience designing and building Multi-Agent systems, Agent-to-Agent (A2A) communication, and orchestration frameworks (e.g., LangChain, LangGraph). * Proven track record building multi-tiered, enterprise-grade full-stack systems. * Strong knowledge of distributed systems, microservice architecture, and cloud platforms (AWS/Azure/GCP). * Demonstrated ability to partner with business/product stakeholders and translate business requirements into system designs. * Modern software engineering practices: API design, event-driven architecture, CI/CD, observability. * Experience delivering in Agile/Scrum environments with aggressive timelines. * Bachelor's degree in Computer Science or related field (or equivalent experience). Preferred * Proficiency with AI tracing and evaluation tools (LangSmith, Arize, HoneyHive, or custom OpenTelemetry implementations). * Knowledge of Quote-to-Cash (Q2C) transformation and revenue operations. * Experience with GTM, Sales, Partner/Channel Sales, or services business processes. * Experience establishing golden datasets and performing comparative analysis across models. * Familiarity with building supervised, unsupervised, and semi-supervised models. * Excellent communication skills with the ability to influence at all levels. ## Description We are hiring AI Engineers at multiple levels (Lead/Staff through Senior Principal) 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. Your Impact AI Architecture & Solution Design * Business-to-Architecture Translation: Partner with business and product teams to deeply understand requirements. Translate business intent into scalable, modern technical architectures that meet non-functional requirements (performance, reliability, scalability, security, observability). * AI Gateway Architecture: Architect a Proxy-First AI Ecosystem-an intermediary layer that ensures vendor independence, allowing seamless switching between LLMs (e.g., GPT-4, Llama, Anthropic) without refactoring core application code. * Unified API Design: Build a single, unified API endpoint that abstracts the complexities of individual LLM providers, providing centralized control for access and security. * Technology Selection: Evaluate and choose the right architecture patterns for each use case-selecting from agentic, RAG, workflow-based, hybrid, or traditional engineering approaches based on problem characteristics. * Build vs. Buy Decisions: Make principled technology choices. Design for maintainability, extensibility, and operational excellence. GenAI & Multi-Agent Systems * Agentic AI Development: Design and build sophisticated Multi-Agent Systems and Agent-to-Agent (A2A) workflows capable of planning, multi-step execution, self-correction, and collaboration with humans or other agents. * Business Workflow Automation: Apply GenAI to complex GTM business logic-automating workflows such as customer support, sales compensation, entitlement platforms, and partner channel operations. * Lead-to-Cash Transformation: Own end-to-end delivery of MarTech and FinTech initiatives, from technical design to operational readiness, leveraging AI to transform quote-to-cash processes. * Advanced Reasoning Pipelines: Develop retrieval systems, knowledge graph integrations, and reasoning pipelines that support autonomous task execution. AI Observability & Quality * LLM Observability: Implement comprehensive observability pipelines for GenAI-tracking trace-level data, prompt inputs/outputs, model latency, and cost. * Cost & Performance Optimization: Design architecture that routes queries to the optimal model based on complexity, balancing performance and expense. * Evaluation Frameworks: Pioneer "LLM as a Judge" testing methodologies-using capable models to evaluate the correctness, tone, and helpfulness of system outputs. Establish golden datasets for ground truth testing. * Feedback Loops: Integrate observability data into the development cycle to identify bottlenecks, high-latency chains, and model drift in real-time. Security & Governance * AI Safety Implementation: Build robust guardrails to filter inputs and outputs-preventing PII exposure, offensive content, and prompt injection attacks. This is a cybersecurity company; security-first thinking is non-negotiable. * Adversarial Defense: Develop detection mechanisms including keyword filtering, behavioral analysis, and adversarial training to protect model instructions from manipulation. * Graceful Degradation: Design systems that are resilient to AI component failures with proper fallbacks. Leadership & Communication * End-to-End Ownership: Drive solution development from prototype to production, ensuring scalability, reliability, and performance. * Stakeholder Communication: Communicate complex technical decisions and trade-offs clearly to both engineering peers AND business stakeholders/executives. * Mentorship: Mentor and guide engineers, elevating engineering practices across the organization. * Cross-Team Influence: Lead high-ambiguity, cross-team technical initiatives and drive alignment. ## Related Videos - [Go with the Flow: Stop the Leaks Before Your Memory's a Waterfall!](https://www.wearedevelopers.com/videos/100073-go-with-the-flow-stop-the-leaks-before-your-memory-s-a-waterfall) - [Stop using Node.js like in 2020! What changed and what you can do today with Node.js](https://www.wearedevelopers.com/videos/100011-stop-using-node-js-like-in-2020-what-changed-and-what-you-can-do-today-with-node-js) - [ Evaluating AI models for code comprehension](https://www.wearedevelopers.com/videos/1462-evaluating-ai-models-for-code-comprehension) - [The AI-Native Engineering Org: What’s Real, What’s Hype, What’s Next](https://www.wearedevelopers.com/videos/100004-the-ai-native-engineering-org-what-s-real-what-s-hype-what-s-next) - [Building AI Applications with LangChain and Node.js](https://www.wearedevelopers.com/videos/1512-building-ai-applications-with-langchain-and-node-js) - [Speak, Code, Deploy: Transforming Developer Experience with Voice Commands](https://www.wearedevelopers.com/videos/1159-speak-code-deploy-transforming-developer-experience-with-voice-commands) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere)