TrendAI is a global cybersecurity leader, protecting hundreds of thousands of organizations and millions of individuals across clouds, networks, devices, and endpoints. Powered by decades of security expertise and world-leading threat research, we are now taking the next step in our evolution — rebuilding how software itself is built, with AI at the core.
About the role
We are taking AI-driven development (AI-DLC) to the next horizon — applying AI rigorously across design, implementation, testing, and operations to materially raise engineering throughput and product quality. We are hiring a (Senior) AI Engineer to bridge frontier AI capabilities and practical engineering, designing the workflows, infrastructure, and agents that engineers across our organization rely on every day to ship better security products faster.
Your Mission
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Stay current with AI engineering practice; evaluate and pilot emerging tools, models, and workflows.
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Design and ship AI-native development workflows, integrating modern AI coding agents into daily engineering practices.
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Build and operate the AI infrastructure — agents, evaluation harnesses, telemetry, and platform services — that powers AI-native engineering at scale.
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Drive measurable adoption with clear metrics (cycle time, review throughput, defect rates), and partner with teams to onboard.
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Troubleshoot and resolve complex issues across the AI/agent stack; identify root causes and implement durable fixes.
What We’re Looking For
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Bachelor’s degree in Computer Science, Engineering, or equivalent industry experience.
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Proficiency in Python and at least one additional language (e.g., Golang, Java, C/C++).
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Hands-on experience using AI agents in real work - a side project, open-source contribution, or shipped feature you can walk us through.
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Practical experience with AI coding agents (e.g., Claude Code, GitHub Copilot, Codex, Cursor).
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Experience in AI agent design and multi-agent systems.
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Strong expertise in context engineering and harness engineering, and skill/tool design for reliable LLM behavior.
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Strong judgment in evaluating LLM outputs — distinguishing quality, correctness, hallucination, and subtle failure modes.
Extra Skills We Love
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Experience designing retrieval and memory layers around LLMs, with judgment on simplicity-first design.
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Experience building developer tools or AI infrastructure at company scale.