Senior IT AI​/ML Engineer - Data Analytics

Our Mission

At Palo Alto Networks, we’re united by a shared mission—to protect our digital way of life. We thrive at the intersection of innovation and impact, solving real-world problems with cutting-edge technology and bold thinking. Here, everyone has a voice, and every idea counts. If you’re ready to do the most meaningful work of your career alongside people who are just as passionate as you are, you’re in the right place.

In order to be the cybersecurity partner of choice, we must trailblaze the path and shape the future of our industry. This is something our employees work at each day and is defined by our values:
Disruption, Collaboration, Execution, Integrity, and Inclusion. We weave AI into the fabric of everything we do and use it to augment the impact every individual can have. If you are passionate about solving real-world problems and ideating beside the best and the brightest, we invite you to join us!

We believe collaboration thrives in person. That’s why most of our teams work from the office full time, with flexibility when it’s needed. This model supports real‑time problem‑solving, stronger relationships, and the kind of precision that drives great outcomes.

As a Senior IT AI/ML Engineer
, you will be responsible for the hands‑on development, and implementation of AI‑powered solutions that solve challenging business problems across IT and various business functions (Sales, Product, Finance & Marketing). You will work closely with Principal and Data leads to translate high‑level strategies into concrete AI solution designs and robust, scalable, and ethical implementations. This role demands deep technical expertise, a strong ability to solve problems, and collaborate with other engineers, leveraging our AI Platform to deliver measurable business impact.

• AI Solution Implementation:
  Design and implementation of AI solutions, translating business problems into AI designs, and managing model selection, data requirements, and integration for AI applications.

• Platform Development:
  Develop and implement core components of the enterprise AI/ML platform, ensuring scalability and security. Contribute to the lifecycle of traditional and Generative AI model deployment and real‑time inference systems.

• System Optimization:
  Design and optimize large‑scale AI/ML systems for performance, reliability, and developer‑friendliness, focusing on low latency and high throughput in real‑time AI applications.

• Technology Adoption:
  Evaluate and integrate new AI tools, frameworks, and cloud solutions, aligning with architectural guidelines. Perform POCs for emerging AI innovations.

• Architectural Best Practices:
  Champion design standards and best practices for AI systems, collaborate with other AI engineers.

• Technical Expertise:
  Lead technical design discussions, perform code reviews and fostering engineering excellence.

• Cross‑Functional
  
  Collaboration:
  
  Partner with Data Scientists, ML Engineers, Product Managers, and IT stakeholders to develop production‑grade AI solutions.

• Responsible AI & Quality:
  Ensure AI systems comply with responsible AI principles and security policies. Implement automated testing and monitoring.

• Innovation & Research:
  Apply cutting‑edge AI/ML techniques to solve problems and improve solutions, staying updated on advancements in Generative AI and LLMs.

• Deep Technical Expertise
  :
  Strong foundation in machine learning (theory and application), deep learning, statistical modeling, and relevant programming languages (Python, R) and libraries (Tensor Flow, PyTorch, scikit‑learn).

• System Design & Scalability
  :
  Ability to design, build, and deploy scalable and robust AI systems. Understanding of MLOps and data engineering principles.

• Problem Solving & Critical Thinking
  :
  Excellent analytical skills to tackle complex, ambiguous problems and break them down into manageable parts.

• Communication & Collaboration
  :
  Ability to clearly communicate complex technical concepts to diverse audiences (technical and non‑technical) and collaborate effectively with cross‑functional teams.

• Business Acumen & Impact Focus
  :
  Understanding of how AI can drive business value and ability…