Principal Software Engineer, Cloud & Edge Backend
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About the role
WHAT YOU WILL BE DOING
Architect and build the next-generation AI Security platform for enterprise identity and privileged access.
Design and develop highly scalable microservices in Go (Golang) that power the Cloud Control Plane and distributed Edge Backends.
Build the Cloud Control Plane responsible for policy management, AI governance, discovery orchestration, analytics, reporting, and APIs.
Build distributed Edge Backends that operate as local Points of Presence (PoPs) for headquarters, regional hubs, branch offices, and customer data centers.
Design resilient policy synchronization from Cloud → Edge → Endpoint with secure fail-closed enforcement during connectivity failures.
Build enterprise-scale discovery services for cloud resources, on-prem infrastructure, endpoints, applications, databases, and AI agents.
Develop access-brokering capabilities that enable privileged access to applications, machines, and services through local Edge Backends.
Design highly available, observable, and resilient distributed systems using modern cloud-native technologies.
Mentor engineers, lead architecture discussions, and drive engineering best practices.
AI & Agentic Engineering
Leverage AI-assisted software development throughout the SDLC to improve productivity, code quality, testing, documentation, and operational excellence.
Build platform capabilities to discover, govern, and secure AI agents, automation frameworks, MCP servers, and agentic applications.
Develop scalable services supporting AI-native enterprise workloads.
Follow AI SDLC best practices across design, implementation, testing, deployment, and operations.
WHAT YOU BRING
1+ years of Principal-level backend software engineering experience with a SaaS company.
Expert-level Go (Golang) development.
Deep experience building distributed systems and microservices.
Experience with Kubernetes, Docker, gRPC, REST APIs, and cloud-native platforms.
Experience with PostgreSQL, Redis, Kafka/NATS or similar distributed infrastructure.
Strong understanding of networking, authentication, authorization, TLS, proxies, and distributed caching.
Experience building enterprise security, IAM, PAM, Zero Trust, or networking products is highly desirable.
Hands-on experience using AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, Windsurf, ChatGPT, or similar.
Understanding of modern AI application architectures, including LLMs, RAG, AI Agents, orchestration frameworks, and Model Context Protocol (MCP).
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 technical leadership, architecture, mentoring, and communication skills.
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