Palo Alto Networks
Principal AI Engineer, Enterprise AI Platform
Palo Alto Networks, Santa Clara, California, us, 95053
Principal AI Engineer, Enterprise AI Platform
At Palo Alto Networks® everything starts and ends with our mission: to be the cybersecurity partner of choice, protecting our digital way of life. We are a company built on challenging and disrupting the way things are done, and we’re looking for innovators who are as committed to shaping the future of cybersecurity as we are.
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.
System Design & Optimization: Contribute significantly to the detailed design of large‑scale, distributed AI/ML systems, ensuring performance, reliability, security, and developer‑friendliness.
Rapid Experimentation & Integration: Evaluate and integrate new AI tools, frameworks, SDKs, and cloud solutions into the platform.
Architectural Adherence & Best Practices: Champion and enforce design standards, patterns, and best practices for scalable and secure development of AI assistants, agents, and applications.
Technical Leadership & Mentorship: Provide technical leadership and mentorship to other AI and ML engineers.
Cross‑Functional Collaboration: Partner effectively with executive leadership, data science teams, engineering, product stakeholders, and business leaders.
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 research and evaluate cutting‑edge AI/ML techniques, algorithms, and models 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.
Qualifications
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.
Deep practical understanding of the full AI lifecycle for developing assistants, agents, and applications.
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, multi‑modal models, retrieval‑augmented generation, and agentic AI systems.
Proficiency in modern AI/ML frameworks (TensorFlow, PyTorch, JAX) and cloud AI/ML platforms (AWS SageMaker, Google Cloud AI Platform/Vertex AI, Azure ML).
Strong programming skills in 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.
Preferred Qualifications
Master’s degree or Ph.D. in Computer Science, Machine Learning, or a related technical field.
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.
Job Details Senior‑level: Mid‑Senior level Employment type: Full‑time Industry: Computer and Network Security
EEO Statement Palo Alto Networks is an equal opportunity employer. We celebrate diversity in our workplace, and all qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or other legally protected characteristics. All your information will be kept confidential according to EEO guidelines.
Immigration Sponsorship Is role eligible for Immigration Sponsorship? No. Please note that we will not sponsor applicants for work visas for this position.
#J-18808-Ljbffr
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.
System Design & Optimization: Contribute significantly to the detailed design of large‑scale, distributed AI/ML systems, ensuring performance, reliability, security, and developer‑friendliness.
Rapid Experimentation & Integration: Evaluate and integrate new AI tools, frameworks, SDKs, and cloud solutions into the platform.
Architectural Adherence & Best Practices: Champion and enforce design standards, patterns, and best practices for scalable and secure development of AI assistants, agents, and applications.
Technical Leadership & Mentorship: Provide technical leadership and mentorship to other AI and ML engineers.
Cross‑Functional Collaboration: Partner effectively with executive leadership, data science teams, engineering, product stakeholders, and business leaders.
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 research and evaluate cutting‑edge AI/ML techniques, algorithms, and models 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.
Qualifications
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.
Deep practical understanding of the full AI lifecycle for developing assistants, agents, and applications.
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, multi‑modal models, retrieval‑augmented generation, and agentic AI systems.
Proficiency in modern AI/ML frameworks (TensorFlow, PyTorch, JAX) and cloud AI/ML platforms (AWS SageMaker, Google Cloud AI Platform/Vertex AI, Azure ML).
Strong programming skills in 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.
Preferred Qualifications
Master’s degree or Ph.D. in Computer Science, Machine Learning, or a related technical field.
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.
Job Details Senior‑level: Mid‑Senior level Employment type: Full‑time Industry: Computer and Network Security
EEO Statement Palo Alto Networks is an equal opportunity employer. We celebrate diversity in our workplace, and all qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or other legally protected characteristics. All your information will be kept confidential according to EEO guidelines.
Immigration Sponsorship Is role eligible for Immigration Sponsorship? No. Please note that we will not sponsor applicants for work visas for this position.
#J-18808-Ljbffr