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As a Senior or Principal Software Engineer in Cortex Cloud, you will contribute to the development and scaling of cloud-native security solutions for enterprise organizations. This role involves working within an established team to evolve a high-traffic product, with a focus on refining architecture, optimizing the technology stack, and maintaining engineering standards. Your responsibilities include writing reliable code, influencing product direction, and designing distributed systems. You will be expected to make technical decisions that impact the long-term stability and performance of cloud workload protection services. A core component of our development process is the use of AI. Rather than basic code completion, we integrate AI assistants as functional components of our workflow. Our team utilizes a multi-agent AI system (IDEX/ProDex) that assists across the development lifecycle: from planning and architecture to code analysis and security reviews. In this role, you will: Work with AI Tools:Utilize platforms such as Gemini, Claude, and Cursor for tasks beyond code generation, including root-cause analysis, system design reviews, and architectural assessment. Develop AI-Augmented Workflows:Help refine how AI is integrated into the SDLC, including the orchestration of agents and the development of internal tools that extend AI capabilities across our codebase. Maintain Quality Standards:While AI assists in increasing velocity, you are responsible for the technical output. This includes critical review of all generated code and ensuring that AI-assisted work aligns with our architectural requirements and security benchmarks. Interact with Specialized Agents:Coordinate with AI agents (Product, Architecture, Security) that operate on shared context to assist in managing complex engineering tasks. We are looking for engineers who are interested in leveraging AI as a technical tool to manage complexity and who want to contribute to the practical application of human-AI collaboration in a cloud environment.
Job Responsibility:
Contribute to the development and scaling of cloud-native security solutions for enterprise organizations
Work within an established team to evolve a high-traffic product, with a focus on refining architecture, optimizing the technology stack, and maintaining engineering standards
Write reliable code, influence product direction, and design distributed systems
Make technical decisions that impact the long-term stability and performance of cloud workload protection services
Work with AI Tools: Utilize platforms such as Gemini, Claude, and Cursor for tasks beyond code generation, including root-cause analysis, system design reviews, and architectural assessment
Develop AI-Augmented Workflows: Help refine how AI is integrated into the SDLC, including the orchestration of agents and the development of internal tools that extend AI capabilities across our codebase
Maintain Quality Standards: Critical review of all generated code and ensuring that AI-assisted work aligns with our architectural requirements and security benchmarks
Coordinate with AI agents (Product, Architecture, Security) that operate on shared context to assist in managing complex engineering tasks
Requirements:
5+ years of experience building and maintaining production-grade distributed systems
Proficiency in Go (Golang) is a strong advantage
We are open to engineers with deep expertise in other backend languages (Java, Python, Rust, C#, or Node.js) who are willing to transition to a Go-primary stack and have a focus on clean, well-tested code
Strong grasp of system design, data structures, and algorithms in high-scale cloud environments
Experience with CI/CD, comprehensive testing (unit, integration, E2E), and rigorous code reviews
Proficiency in AWS, GCP, or Azure, including cloud-native services
Experience with observability (monitoring, logging, tracing) and system profiling
B.Sc. or M.Sc. in Computer Science, Software Engineering, or equivalent technical/military experience
Nice to have:
Advanced Go: Deep experience with concurrency and memory management patterns
Distributed SaaS: Background in managing multi-tenant, cloud-based SaaS at scale
Cybersecurity: Familiarity with threat detection or cloud security infrastructure
AI Systems: Interest in agentic workflows or prompt engineering in production