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Position Overview As a global cybersecurity leader, Trend Micro actively embraces AI to enhance how we build products and solve complex security challenges. You'll be at the forefront of this evolution, serving as the bridge between emerging AI capabilities and practical engineering applications.

We are seeking an AI Engineer to research cutting-edge AI/LLM technologies, build AI infrastructure and tools, and enable our teams to embrace AI-powered development workflows.

Your Mission LLM Fine-Tuning & Optimization

• Fine-tune and optimize LLMs and SLMs for cybersecurity use cases
• Develop fine-tuning strategies and conduct model evaluation
• Work with parameter-efficient fine-tuning methods (LoRA, QLoRA, PEFT)
• Optimize model inference latency and resource utilization

AI-Powered Development & Tooling

• Design and implement AI-native development workflows using tools like Copilot, Claude Code, Codex
• Build AI agents and tools that accelerate software development
• Develop APIs, SDKs, and automation tools powered by AI/LLM
• Integrate AI capabilities into existing development pipelines

AI Research & Innovation

• Research and evaluate cutting-edge AI/LLM technologies
• Design and execute proof-of-concepts (POCs)
• Troubleshoot and debug complex AI-related issues

Education & Experience

• Bachelor's or Master's degree in Computer Science, Engineering, AI, Machine Learning, or equivalent
• 1+ years of hands-on experience in machine learning and AI development

Core Technical Skills (Required)

• Hands-on experience with LLM fine-tuning and optimization
• Practical experience using AI tools for software development (Copilot, Claude Code, Codex, or similar)
• Proficiency in Python
• Experience with deep learning frameworks (PyTorch, TensorFlow)
• Understanding of transformer architectures and LLM fine-tuning frameworks (HuggingFace, vLLM)
• Strong expertise in prompt engineering and AI agent development
• Familiarity with Git and CI/CD processes

Preferred Skills

• Experience in AI agent design and multi-agent systems
• Experience with vector databases (Pinecone, Weaviate, ChromaDB)
• Knowledge of RAG architectures and MCP integration
• Cloud platform experience (AWS, Azure, GCP)
• Experience with containerization (Docker, Kubernetes)
• GPU programming and optimization (CUDA)

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