Sr. Architect, AI Engineering: Zscaler

Zscaler is a global cloud security leader that operates the world’s largest security cloud. Founded in 2007, their mission is to make the cloud a safe place for business. Serving thousands of enterprise customers, including 45% of Fortune 500 companies, Zscaler's AI-powered Zero Trust Exchange™ platform protects users, devices, and applications from cyberattacks and data loss.

About the Role:
As a Senior Architect for AI Engineering, you will play a pivotal role in shaping how Zscaler leverages cutting-edge deep learning and multimodal AI to secure large-scale distributed systems. This technical leadership role involves influencing product strategy, mentoring global teams, and delivering innovations to safeguard enterprises in dynamic cloud environments.

Responsibilities:

Define, architect, and lead the technical strategy for end-to-end AI solutions to detect, prevent, and mitigate security risks in cloud-native environments.

Design, train, and deploy advanced deep learning and multimodal AI models for threat and anomaly detection, optimizing them for performance and scalability.

Build and implement AI-driven solutions for critical cloud security use cases like intrusion detection and zero-day vulnerability analysis.

Partner with product, engineering, and research teams to integrate AI innovation into security products.

Represent the company as a recognized expert in AI for cloud security at conferences and with strategic customers.

Requirements:

Minimum Qualifications:

Ph.D. or equivalent experience in AI/ML, Computer Science, or a related field, with a strong focus on deep learning or security applications.

A proven track record of delivering and optimizing large-scale AI systems in production.

Deep expertise in major deep learning frameworks (e.g., PyTorch, TensorFlow) and deploying models in distributed cloud environments (AWS, Azure, GCP).

Strong working knowledge of critical cloud security domains (e.g., IAM, workload protection, network security).

Exceptional communication and interpersonal skills.

Preferred Qualifications:

Deep expertise in applying Large Language Models (LLMs) and multimodal foundation models to cybersecurity use cases.

Familiarity with cutting-edge security and privacy technologies for AI, such as confidential computing and privacy-preserving machine learning.

Demonstrated industry influence through open-source contributions, publications, patents, or thought leadership.