AI Native Fullstack Engineer (Security Operations)

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.