Overview 

The XDR capabilities of Trend Micro’s Vision One platform provide context-aware investigation, recording, and reporting of system-level activities across multiple security layers — AI App, endpoint, servers, cloud workloads, email, and networks. We’re evolving how the team builds software. This isn’t a role defined by a technology layer — it’s defined by how you think and work. AI is your primary tool for accelerating the full engineering cycle, and system thinking is how you use it responsibly. You’ll start with a domain you’re strong in and grow from there. 

[Your Mission] 

- Start with one or two anchor domain — backend, frontend, or DevOps — and go deep. Over time, use AI as a bridge to build working knowledge across all three, not to replace specialists, but to collaborate without walls. 

- Invest in understanding the product domain and user workflows deeply enough to translate requirements into precise system specs — using BDD/TDD as the shared language between human intent and AI generation. 

- Integrate AI tools (e.g., GitHub Copilot, Claude Code) throughout the development lifecycle — from spec writing and test generation to code review and debugging — rather than using them as occasional shortcuts. 

- Own the full engineering cycle: from requirement to spec, AI-assisted implementation, architecture-aware review across frontend, backend, QA, and DevOps, through to data-driven validation that what we shipped actually solves the problem. 

- Use data as a feedback loop — monitor service signals (reliability) and track feature usage to validate outcomes, and feed those insights back into the next iteration. 

[Your Entry Point] 

- Pick the domain you’re strongest in. This is where you’ll go deep first. 

- Backend — Experience in Java and AWS cloud services. 

- Frontend — Experience in browser JavaScript development, including React and core web technologies (HTML/CSS/JS). 

- DevOps — Experience in Docker/K8S/CI/CD and cloud infrastructure operations. 

[Required — All Tracks] 

- Bachelor’s degree or above in computer science or related fields. 

- Hands-on experience with AI-native development tools (e.g., Cursor, GitHub Copilot, Claude Code) in a real project or side project. 

- Ability to write clear BDD/TDD specs that scope AI-assisted development — understanding that the quality of specs determines the quality of AI output. 

- Ability to provide effective context when working with AI — understanding that output quality depends on input quality. 

- Habit of critically reviewing AI-generated code rather than adopting it directly, with awareness of system-wide concerns such as API contracts, data models, and security boundaries. 

[Big Plus] 

- Working knowledge beyond your anchor domain — frontend, backend, or DevOps. 

- Familiar with cloud infrastructure such as networking, security, and computing stack. 

- Experienced in operating products/services at large scale. 

- Deep understanding of networking and Linux operating systems. 

- Understanding of agent frameworks or common agent design patterns . 

- Familiarity with harness engineering techniques to guide AI toward higher-quality outputs.