Responsibilities
- Develop distributed systems that manage identity and access controls for AI agents operating autonomously.
- Formulate zero trust frameworks governing interactions between agents and between agents and backend systems.
- Design cryptographic identity solutions, verifiable credentials, and secure delegation mechanisms.
- Build and deploy production-ready, multi-tenant SaaS platforms with high availability.
- Develop containerized microservices on Kubernetes with strong monitoring, scaling, and fault tolerance.
- Implement secure communication protocols between services using cloud-native security standards.
- Architect complex workflows involving multiple AI agents to evaluate identity and governance limits.
- Construct adversarial testing environments to simulate attacks like prompt injection and privilege escalation.
- Ensure system resilience against realistic enterprise-level security threats.
- Develop APIs and SDKs that integrate secure identity functions into AI-driven applications.
- Maintain clean, modular abstractions for smooth integration with enterprise infrastructure.
- Design secure interaction models between agents and external systems via MCP or custom tooling.
- Guarantee all tool executions are authenticated, authorized, and fully traceable.
- Define and enforce policies for tool usage and resource access.
- Lead technical vision and long-term architectural planning for identity systems.
- Set engineering benchmarks for security, system reliability, and performance efficiency.
- Collaborate with infrastructure, product, and security teams to ensure scalable implementation.
- Guide and mentor engineering teams to elevate technical capabilities across the organization.
Benefits
- Generous time off policies
- Top shelf benefits
- Education, wellness and lifestyle support
