Associate Principal Software Engineer, Cloud & Edge Backend

Saviynt's AI-powered identity platform manages and governs human and non-human access to all of an organization's applications, data, and business processes. Customers trust Saviynt to safeguard their digital assets, drive operational efficiency, and reduce compliance costs. Built for the AI age, Saviynt is today helping organizations safely accelerate their deployment and usage of AI. Saviynt is recognized as the leader in identity security, with solutions that protect and empower the world’s leading brands, Fortune 500 companies and government institutions. For more information, please visit .

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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Architect and build the next-generation AI Security platform for enterprise identity and privileged access.

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Design and develop highly scalable microservices in Go (Golang) that power the Cloud Control Plane and distributed Edge Backends.

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Build the Cloud Control Plane responsible for policy management, AI governance, discovery orchestration, analytics, reporting, and APIs.

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Build distributed Edge Backends that operate as local Points of Presence (PoPs) for headquarters, regional hubs, branch offices, and customer data centers.

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Design resilient policy synchronization from Cloud → Edge → Endpoint with secure fail-closed enforcement during connectivity failures.

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Build enterprise-scale discovery services for cloud resources, on-prem infrastructure, endpoints, applications, databases, and AI agents.

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Develop access-brokering capabilities that enable privileged access to applications, machines, and services through local Edge Backends.

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Design highly available, observable, and resilient distributed systems using modern cloud-native technologies.

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Mentor engineers, lead architecture discussions, and drive engineering best practices.

AI & Agentic Engineering

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Leverage AI-assisted software development throughout the SDLC to improve productivity, code quality, testing, documentation, and operational excellence.

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Build platform capabilities to discover, govern, and secure AI agents, automation frameworks, MCP servers, and agentic applications.

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Develop scalable services supporting AI-native enterprise workloads.

-Follow AI SDLC best practices across design, implementation, testing, deployment, and operations.r