AI Engineer

Overview

Live the experience
. From professional empowerment to continual learning opportunities. From ongoing investment in new and emerging technologies to a career of self-determination. At Ulta Beauty, our tech team is critical to our scalability—and is recognized that way. We’ve been defined as a "mature start-up." A place where interdepartmental exposure, open doors, and genuine collaboration is ubiquitous. Where challenges come fast and furious, requiring agility, mental dexterity, and creativity.

Where our passion for better solutions drives us and is core to who we are.

We’re engineering for the future of retail, and it’s no-holds-barred. But for those motivated by continual change and ambiguity, by superior leadership, by whip smart colleagues who will press you daily for your very best, you’ll find that virtually nothing’s impossible at Ulta Beauty.

THE IMPACT YOU CAN MAKE:

We're looking for an AI Engineer to join our growing AI Engineering practice at Ulta Beauty. You'll work with in a collaborative, senior-heavy team while owning individual contributions that power critical decisions across the company — building agents, pipelines, and integrations from the ground up.

This is a role for someone who moves forward without waiting for perfect conditions, who figures things out when they don't know the answer, and who understands that credibility is built through consistent delivery — not credentials alone.

YOU’LL ACCOMPLISH THESE GOALS BY:

• Build and iterate on components for LLM-based applications — prompt pipelines, tool integrations, RAG patterns — under the guidance of senior engineers, with attention to code quality and production readiness.
• Contribute to the design and implementation of agent-based solutions across the team’s active technology stack.
• Work within enterprise cloud environments to build and deploy AI components, developing fluency with the platforms and tools the team operates on.
• Participate in code reviews as both reviewer and reviewee, developing the habit of giving and receiving technical feedback.
• Engage actively in team rituals and cross-functional discussions, contributing your perspective and absorbing the reasoning of senior engineers.
• Communicate technical progress clearly to your immediate team and stakeholders.
• Stay current with developments in the AI engineering space and bring relevant findings back to the team.
• Take ownership of your own development: come prepared, follow through on commitments, and surface blockers proactively rather than waiting for check-ins.

ESSENTIALS FOR SUCCESS:

• Bachelor's or master's in computer science, engineering, mathematics, or a related field. Equivalent practical experience will be considered.
• 2+ years of professional software engineering experience in any domain.
• Demonstrable exposure to the AI space — professional, academic, or through personal projects. We are not looking for a specific number of years; we are looking for genuine engagement with the field.
• Solid programming fundamentals in Python, with an understanding of basic software engineering practices: version control, code structure, and testing.
• Some exposure to cloud platforms (GCP, Azure, or AWS) — enough to navigate an environment and understand how services connect.
• Ability to work cross-functionally to understand business goals and communicate solutions that meet business objectives.
• Self-driven with a bias toward action: you move forward with what you have, flag what you’re uncertain about, and course correct as you learn more.
• Intellectual honesty — you say what you think, you acknowledge what you don’t know, and you don’t pretend otherwise.
• A genuine interest in building things that work in production, not just things that work in a notebook.
• Travel may be required (team collaboration / conferences / vendor meetings).

Nice to Have:

• Familiarity with agentic frameworks such as Google ADK, OpenAI Agents SDK, or similar orchestration tooling.
• Experience with tool-use patterns, multi-step agent orchestration, or MCP (Model Context Protocol).
• Exposure to MLOps practices: CI/CD for ML, model monitoring, or pipeline automation.
• Prior work in a retail, e-commerce, or consumer-facing…