AI Solution Security Engineer

Google creates products and services that make the world a better place, and gTech’s role is to help bring them to life.

gTech Risk helps protect Google and its users by identifying and implementing controls for significant risks. To ensure gTech's security and agility, the team analyzes gTech business processes to find control gaps, collaborates with stakeholders to resolve these gaps, and provides risk expertise to engineer solutions.

gTech Risk Solutions Engineering is transforming from a reactive manual process led organization to a proactive engineering powerhouse. Operating as a federated security partner to Google's central security organizations, we design and build contextualized security solutions tailored specifically for the gTech business ecosystem. Our mission is upfront risk prevention through technical delivery excellence, shifting the paradigm from unsustainable "risk-chasing" to "prevention by design."

In the AI Security function, you will serve as the core experts of our Functional Engineering pod (Strategy and Design). You will be the architectural mind behind understanding the risk implications of shifting gTech operations from human first to AI first. You will achieve this via gTech Risk's shift to automated prevention. You will own the overarching system design for autonomous risk identification, AI guardrails, and vulnerability auto-mitigation.

A central focus of your mandate will be architecting the security constraints for gTech's current and future operations. You will design the validation frameworks (including prompt injection resilience) for agentic platforms deployed on global configurations. You will act as the critical bridge to central teams, ensuring our local solutions conform to Google's general security policies while moving at the speed of gTech operations.

The Global Business Organization (GBO) is the engine that powers Google’s full ecosystem of products and services to help customers and partners succeed and grow. GBO includes the commercial arms of Ads sellers, business development teams, and customer services and support, as well as overlay programs that enable coordinated engagement with Google's most complex and important customers and partners.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities

• Design the security architecture and validation frameworks (e.g., prompt injection resilience, data flow constraints) for agentic platforms like our agentic access and vulnerability systems.
• Design technical standards and automated workflows for vulnerability auto-mitigation to ensure alignment with global SLO timelines.
• Architect automated access control systems, integration templates, and data flow constraints to proactively prevent access abuse and data exfiltration, with particular focus on developing holistic mapping of agentic threat models and engineering solutions to prevent and detect before material risks manifest.
• Align all technical architectures with central security design standards, serving as the primary design conduit to central infrastructure.
• Design precise technical designs for the Operational Engineering (Build) team for software fulfillment.

Minimum qualifications:

• Bachelor's degree in Computer Science, Cybersecurity, a related technical field, or equivalent practical experience.
  
• 2 years of experience in security engineering, threat modeling, systems design, or security architecture.
• Experience in software engineering coding in C++, Java, Go, or Python.

Preferred qualifications:

• Master's degree or PhD in Computer Science or a related technical field.
  
• Experience designing security controls for large-scale distributed systems or cloud infrastructure.
  
• Experience architecting enterprise-scale security tools, access control systems, or observability platforms.
  
• Deep technical understanding of Large Language Models (LLMs), AI security risks (prompt injection, data poisoning), and the deployment of deterministic security harnesses.
  
• Strong capability to influence executive engineering partners on critical design decisions.