Come join us as founding members of Saviynt’s AI Security team and help us build out AI security for the world's leading enterprises.
WHAT YOU WILL BE DOING
Build native endpoint agents for Windows, macOS, and Linux.
Develop the integrated privileged access client within the endpoint agent.
Implement Privileged Endpoint Device Management (PEDM) policy enforcement.
Build secure communication between Endpoint Agents and Edge Backends with resilient fail-closed behavior.
Develop local telemetry collection covering applications, processes, services, users, and endpoint posture.
Detect Shadow IT, unauthorized applications, AI agents, automation agents, MCP servers, and developer tooling running on endpoints.
Discover local resource access, including filesystem, browser data, credentials, local databases, network shares, and APIs.
Optimize agent performance, scalability, reliability, and resource utilization.
Lead architecture and mentor engineers building endpoint platform capabilities.
AI & Agentic EngineeringLeverage AI-assisted software development throughout the SDLC.
Build capabilities to discover, inventory, and govern AI agents and agentic workloads executing on enterprise endpoints.
Develop secure endpoint capabilities supporting AI-native enterprise environments.
Apply AI SDLC best practices across development, testing, deployment, and operations.
WHAT YOU BRING
1+ years of Principal-level of systems software engineering experience.
Strong C++, Rust, or Go programming experience.
Deep knowledge of Windows, macOS, or Linux internals.
Experience with endpoint security products, including EDR, XDR, DLP, endpoint management, and PAM/PEDM.
Experience with operating system security, process management, filesystem monitoring, ETW, eBPF, or similar platform technologies.
Strong debugging, performance tuning, and systems engineering skills.
Hands-on experience using AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, Windsurf, ChatGPT, or similar.
Understanding of AI agents, LLMs, MCP, and modern AI application architectures.
Familiarity with AI SDLC best practices, including AI-assisted development, automated testing, secure coding, CI/CD automation, observability, and responsible use of AI-generated code.
Strong leadership, design, and mentoring skills.
