Software Engineer III, Agentic AI Systems, Cloud Security

• Write product or system development code, collaborating with peers and stakeholders through design and code reviews to ensure best practices amongst available technologies (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
• Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
• Implement GenAI solutions, utilize ML infrastructure, and contribute to data preparation, optimization, and performance enhancements.
• Develop and launch of next-generation Agentic features for Google SecOps, building the foundational AI platform to support and accelerate security sgents development.
• Scale the product and defend the enterprises from emerging threats.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
• 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
• Experience with state of the art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).

Preferred qualifications:

• Master's degree or PhD in Computer Science, Artificial Intelligence, or a related field.
• 2 years of experience with data structures and algorithms.
• Experience in highly scalable and available distributed systems, cloud services or data platforms.
  
• Experience in security domains.
• Proven experience in building and deploying Agentic AI systems.

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
• 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging).
• Experience with state of the art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).