Principal AI Engineer, Enterprise AI Platform

Palo Alto Networks•Santa Clara, CA

5d•Onsite

About The Position

As a Principal AI Engineer for the Enterprise AI Platform, you will be a pivotal technical leader driving the identification, design, and hands-on implementation of AI-powered solutions that directly address critical business challenges across IT and all enterprise functions (Sales, Marketing, Finance, HR, Legal, etc.). You will operate where AI innovation, top-tier engineering, and significant business outcomes converge, acting as the technical bridge between complex business problems and cutting-edge AI execution. Leveraging our robust Enterprise AI Platform, you will translate ambiguous problems into concrete AI solution designs, build core components of these solutions, and ensure their successful deployment and measurable impact, supporting both code-based and low-code/no-code approaches.

Requirements

10+ years of experience in software engineering, distributed systems, or enterprise architecture, including at least 5 years focused on leading AI/ML engineering or platform development
Proven expertise in designing and building complex, enterprise-grade AI/ML platforms and applications, ideally across multiple domains (e.g., customer support, finance, sales)
Deep practical understanding of the full AI lifecycle for developing assistants, agents, and applications, including feature engineering, RAGs, model training, evaluation, validation, deployment, and monitoring
Extensive hands-on experience with distributed systems architecture, streaming data platforms, data lakes, and real-time decision engines
Generative AI & Large Language Models (LLMs): Direct hands-on experience with Generative AI technologies, including Large Language Models (LLMs), multi-modal models, RAGs (Retrieval-Augmented Generation), and agentic AI systems. Familiarity with techniques for LLM alignment, fine-tuning, and responsible deployment
Proficiency in modern AI/ML frameworks (e.g., TensorFlow, PyTorch, JAX) and cloud AI/ML platforms (e.g., AWS SageMaker, Google Cloud AI Platform/Vertex AI, Azure ML)
Strong programming skills in languages such as Python, Java, or Go
Excellent communication skills with a track record of influencing technical and cross-functional stakeholders at VP+ levels
System-Level Thinking: Demonstrated ability to think at a system level, understanding complex interdependencies within distributed AI architectures and optimizing end-to-end performance

Nice To Haves

Master's degree or Ph.D. in Computer Science, Machine Learning, or a related technical field or equivalent military experience required
Experience contributing to open-source AI projects or publications in top-tier AI conferences
Prior experience in a technical consulting or solutions architecture role, bridging technical teams and senior business stakeholders
Deep understanding of cybersecurity principles as they apply to AI systems

Responsibilities

Applied AI Solution Design & Architecture: Deeply understand complex business problems and strategic objectives across various enterprise functions. Break down ambiguous AI problems into concrete, actionable AI solution designs, including identifying the appropriate AI models, data requirements, and integration points for end-to-end AI applications
Hands-on Development & Implementation: Lead the hands-on development and implementation of key components of the AI assistant, agent, app, supporting both traditional and Generative AI model development, deployment, and real-time inference systems. Drive the successful integration of experimental AI technologies into production, showcasing tangible business value through rapid prototyping and measurable results
System Design & Optimization: Contribute significantly to the detailed design of large-scale, distributed AI/ML systems, ensuring performance, reliability, security, and developer-friendliness. Optimize existing systems for scalability, efficiency, and maintainability, ensuring the platform's ability to handle massive scale data and inference requests, optimizing for low latency and high throughput for real-time AI applications
Rapid Experimentation & Integration: Evaluate and integrate new AI tools, frameworks, SDKs, and cloud solutions into the platform, ensuring alignment with architectural strategy and engineering needs. Lead proof-of-concepts (POCs) for emerging AI innovations and drive their integration into production through long-term architectural evolution
Architectural Adherence & Best Practices: Champion and enforce design standards, patterns, and best practices for scalable and secure development of AI assistants, agents, and applications across various teams
Technical Leadership & Mentorship: Provide technical leadership and mentorship to other AI and ML engineers, fostering a culture of engineering excellence, innovation, and hands-on experimentation within the team and across the company
Cross-Functional Collaboration: Partner effectively with executive leadership, data science teams, engineering, product stakeholders, and business leaders to translate complex business use-cases into scalable, production-grade AI solutions. Act as a go-to expert for complex AI engineering challenges, providing technical guidance and hands-on support
Responsible AI & Governance: Implement features and practices that ensure AI systems comply with responsible AI principles, data governance, privacy laws, security policies, and ethical AI frameworks.
Innovation & Research: Actively learn and incorporate advances in Agents, Generative AI, LLMs, and scalable AI architectures. Actively research and evaluate cutting-edge AI/ML techniques, algorithms, and models (e.g., foundation models, multi-modal AI) to identify opportunities for platform enhancement and new solution development
MLOps and Automation: Lead the implementation and continuous improvement of MLOps pipelines, including automated model training, versioning, deployment, and monitoring, to streamline the AI lifecycle. Design and implement automated testing strategies for AI models and platform components to ensure model quality, robustness, and drift detection