Applied AI Engineer - Austin, TX

Trend Micro•Austin, TX

6d•Hybrid

About The Position

TrendAI™, the global AI security leader and enterprise business unit of Trend Micro, empowers organizations with full AI visibility and consolidated security that inspires confidence, drives innovation, and eliminates risk. At TrendAI™, we’re always seeking exceptional talent; people who want to collaborate with the best and push boundaries together. Here, your work goes beyond building a career. You will help protect what matters and play a vital role in shaping a safer, more trustworthy AI-powered future. AI Fearlessly. This is a hybrid role based in our Austin, TX office with an expectation of 3 days onsite per week. Do you love creating elegant solutions to highly complex challenges? Do you instinctively see the importance in every detail? We are looking for Applied AI Engineers to help define how engineers build with AI across our organization. In this role, you’ll sit at the intersection of Generative AI and platform engineering, designing the tools, workflows, and standards that make it easy for developers to build, deploy, and scale AI-powered applications. Your work will directly shape how teams adopt and use AI making it more accessible, reliable, and impactful. You’ll join a lean, high-impact team where collaboration is key. We work closely with engineering, infrastructure, and security partners to ensure the solutions we build are scalable, secure, and solve real-world problems.

Requirements

5+ years of software development experience
Strong programming skills and engineering fundamentals
Experience with modern software delivery practices (CI/CD, cloud environments, etc.)
Strong problem-solving skills and attention to detail
Ability to collaborate across teams and influence without direct authority
Curiosity and a continuous learning mindset

Nice To Haves

Familiarity or interest in AI/ML systems, particularly LLM-based applications
Experience with workflow frameworks such as Temporal.io
Experience with golang & python

Responsibilities

Define the AI Development Lifecycle: Establish and evolve the end-to-end AI development lifecycle, including guardrails, context management, and engineering standards that enable responsible and efficient AI adoption.
Drive Adoption & Alignment: Lead cross-functional initiatives that improve how teams build and ship software, with a focus on scaling AI capabilities and enhancing developer experience.
Build for Developers: Design and deliver self-service capabilities that simplify how engineers build, test, and deploy applications—especially AI-powered ones. This includes reusable tooling, templates, and patterns that reduce friction and accelerate delivery.
Raise the Engineering Bar: Set and uphold high standards for platform and AI engineering. Mentor engineers and stakeholders, and promote clean, scalable, and maintainable design practices.
Think Like a Product Owner: Treat internal platforms and AI capabilities as products. Continuously iterate based on developer feedback, usability, and adoption metrics.
Enable AI at Scale: Develop the foundational components that allow teams to safely and effectively leverage AI, including shared services, reusable modules, and well-defined integration patterns.