Staff Full Stack – AI Engineer
Pocket Prep
MongoDBNode.jsNoSQLPythonTypeScript
Senior Architect, AI Engineering
Zscaler
full-time
Posted on:
Location: 🇺🇸 United States
Visit company websiteSalary
💰 $231,000 - $330,000 per year
Job Level
Senior
Tech Stack
AWSAzureCloudCyber SecurityDistributed SystemsGoogle Cloud PlatformPyTorchTensorflow
About the role
- Define, architect, and lead the technical strategy for end-to-end AI solutions that detect, prevent, and mitigate security risks in cloud-native environments, including proactively addressing emerging threats like Agentic AI.
- Design, train, and deploy advanced deep learning and multimodal AI models for threat and anomaly detection, optimizing them for performance, scalability, and low-latency inference in production.
- Build and implement AI-driven solutions for critical cloud security use cases, such as intrusion detection, zero-day vulnerability analysis, and real-time attack surface reduction.
- Partner with product, engineering, and research teams to integrate AI innovation into security products and cultivate internal technical excellence.
- Represent the company as a recognized expert in AI for cloud security, engaging with strategic customers, conferences, and industry bodies.
Requirements
- Ph.D. or equivalent experience in AI/ML, Computer Science, or a related field, with a strong focus on deep learning, multimodal models, or security applications.
- Proven track record of delivering and optimizing large-scale AI systems in production, including hands-on experience with model compression techniques for efficient deployment.
- Deep expertise in major deep learning frameworks (e.g., PyTorch, TensorFlow), deploying models in distributed cloud environments (e.g., AWS, Azure, GCP), and experience with MLOps and observability tools.
- Strong working knowledge of critical cloud security domains, including Identity and Access Management (IAM), workload protection, network security, and threat intelligence.
- Exceptional communication and interpersonal skills, with the ability to influence diverse stakeholders from executives to global engineering teams.
- 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, including confidential computing, secure enclaves, and privacy-preserving machine learning.
- Demonstrated industry influence through open-source contributions, recognized publications, patents, or thought leadership in AI or security.
