Applied AI Architect - Austin, TX

Trend Micro•Austin, TX

6d•Hybrid

About The Position

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. Proactive security starts here. TrendMicro.com Location: This is a hybrid role based out of our Austin, TX office and requires in-office presence three days a week. Position Summary: 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 optimization 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.

Requirements

Proven end-to-end experience bringing LLM/SLM research into production — from fine-tuning and inference optimization to evaluation and AI Ops integration.
Excellent knowledge on at least one of the following: Deep understanding of data-model-infrastructure trade-offs and optimization 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 toolchains (ex: Weights & Biases, MLflow, Ray Serve, BentoML).
Proficiency in Python, and experience building containerized AI microservices (ex: Docker, Kubernetes, Ray).
3+ years of applied AI/ML research or engineering, including 2+ years in production-scale deployment.

Nice To Haves

Demonstrated success in building scalable infrastructure and launching LLM/SLM-based features and agent systems within enterprise platforms.
Expertise in quantization, 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 (LangChain, AWS Strands, AutoGen, etc).
Deep understanding of A2A/MCP protocols for interoperable multi-agent systems.

Responsibilities

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 operationalize 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 behavior.
Publish, document, and codify reusable AI blueprints for hybrid (cloud + on-prem) deployments and future research acceleration.