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