Applied AI Architect - Austin, TX

Trend Micro, a global cybersecurity leader, helps make the world safe for exchanging digital information across enterprises, governments, and consumers.

Fueled by decades of security expertise, global threat research, and continuous innovation, Trend harnesses AI to protect organizations and individuals across clouds, networks, devices, and endpoints.

The Trend Vision One™ enterprise cybersecurity platform accelerates proactive security outcomes by predicting and preventing threats across the entire digital estate and environments like AWS, Google, Microsoft, and NVIDIA.

Location: This is a hybrid role based out of our Austin, TX office and requires in‑office presence three days a week.

Trend Micro is seeking an Applied AI Architect with deep experience bridging LLM/SLM model research and enterprise productization. You will lead the technical direction from model architecture selection, fine‑tuning, and optimisation to deployment and observability, shaping the next generation of agentic AI for cybersecurity. This role demands both foundation knowledge acumen and production practicality — designing and validating novel approaches, then translating them into reliable, scalable solutions deployed in Trend product platform.

• Drive research‑to‑production of LLM/SLM systems — from design and fine‑tuning to evaluation, deployment, and continual adaptation in enterprise agent workflows.
• Lead technical choices — determine when to apply context engineering, prompt tuning, continued pretraining, supervised fine‑tuning, reasoning fine‑tuning, LoRA, or RL.
• Architect high‑performance inference and serving using vLLM, NVIDIA NIM, Triton, CUDA, or other optimized frameworks.
• Integrate reinforcement learning frameworks (veRL, SkyRL, Torch, Ray RLlib) to enhance reasoning, adaptability, and agent feedback loops.
• Develop and ope rationalise AI Ops pipelines — build benchmark and metrics for model evaluation, observability, drift detection, and lifecycle automation.
• Advance agent interoperability using A2A (Agent‑to‑Agent) or MCP (Model Context Protocol) for large‑scale coordination.
• Collaborate with cybersecurity researchers to embed threat reasoning, anomaly detection, and defensive logic directly into model behaviour.
• Publish, document, and codify reusable AI blueprints for hybrid (cloud + on‑prem) deployments and future research acceleration.

• Proven end‑to‑end experience bringing LLM/SLM research into production — from fine‑tuning and inference optimisation to evaluation and AI Ops integration. Excellent knowledge on at least one of the following:
  • Deep understanding of data‑model‑infrastructure trade‑offs and optimisation under real business constraints.
  • Hands‑on with at least one fine‑tuning or adaptation framework (ex: LLaMA Factory, NeMo, PEFT, LoRA, Transformers).
  • Strong knowledge of GPU‑accelerated inference (ex: vLLM, NIM, Triton, CUDA, NCCL, PyTorch/XLA).
  • Familiarity with AI Ops tool chains (ex: Weights & Biases, MLflow, Ray Serve, Bento
    
    ML).
• Proficiency in Python, and experience building containerised AI micro‑services (ex: Docker, Kubernetes, Ray).
• 3+ years of applied AI/ML research or engineering, including 2+ years in production‑scale deployment.

• Demonstrated success in building scalable infrastructure and launching LLM/SLM‑based features and agent systems within enterprise platforms.
• Expertise in quantisation, distillation, or GPU profiling to lower inference cost.
• Clear conceptual understanding of when to fine‑tune vs prompt‑engineer vs use RLHF — and evidence of having applied each effectively.
• Familiarity with agentic frameworks (Lang Chain, AWS Strands, Auto Gen, etc).
• Deep understanding of A2A/MCP protocols for interoperable multi‑agent systems.

• Research‑driven yet delivery‑focused — capable of balancing innovation with practical deployment.
• Data‑ and results‑oriented — every hypothesis must be measurable.
• Ownership mentality — from exploration and experiment to evaluation, optimisation, and monitoring.
• Passionate about turning AI research into defensible, intelligent, and proactive cybersecurity systems.