Lead AI Engineer

Ulta Beauty, Inc. • Bolingbrook, IL

7d

About The Position

Ulta Beauty is hiring a Lead Artificial Intelligence Engineer to direct complex AI initiatives from exploratory data science and statistical modeling through production deployment and scaling. The role provides technical leadership for advanced recommendation and decision engines, and architects enterprise generative AI solutions using large language models, including fine-tuning, retrieval-augmented generation (RAG), and prompt engineering. The position shares responsibility for AI engineering governance and standards.

The engineer owns enterprise-readiness requirements such as low-latency inference, high throughput, data privacy compliance, and cloud cost efficiency. The role builds production ML pipelines, automated training and CI/CD practices, monitoring for data drift and model degradation, and team-wide standards for code quality, testing, documentation, reviews, and reproducibility.

Working with business and IT product teams, the engineer translates use cases into technical requirements, identifies risks, sets technical roadmaps and estimates, and serves as a decision-maker, escalation point, and technical authority. The role also evaluates emerging technologies, promotes AI innovation within Agile delivery, and communicates technical results and architectural trade-offs to senior stakeholders.

Requirements

• Bachelor’s degree in Computer Science, Mathematics, Physics, Statistics, or another quantitative field.
• Seven years of experience in data science, machine learning, or AI.
• Seven years of experience writing production-quality object-oriented code in Python or C++ and working with ML frameworks such as scikit-learn or TensorFlow, including five years building low-latency or real-time ML systems.
• Seven years of experience designing, building, and deploying large-scale recommender or decisioning systems, including ranking, clustering, and other unsupervised learning methods.
• Seven years of experience building and deploying production ML systems on Google Cloud, Azure, or AWS.
• Six years of experience applying optimization techniques in production environments, including operational research, offer optimization, or revenue maximization in large-scale ML or decisioning systems.
• Five years of experience working in the retail industry.
• Five years of experience building scalable, production-grade real-time inference services using Docker, CI/CD, and monitoring.
• Three years of experience as a Senior AI or ML Engineer, with demonstrated ability to drive technical direction and own AI technical solutions.
• Three years of experience designing and developing reinforcement learning models in real-world or production settings.
• Two years of experience working with large language models (LLMs).
• Two years of experience building or integrating large-scale batch ML pipelines using cloud-native event-driven systems such as Pub/Sub and containerized execution such as GKE.
• Two years of experience developing agentic solutions within Databricks and Google Gemini Enterprise using ADK and LangGraph, including multi-agent orchestration patterns.
• Two years of experience partnering with AI Architects and Product teams to translate requirements into agentic design patterns and define orchestration and agentic automation behavior.
• Experience with large-scale data processing and analytics platforms, including Apache Spark and Databricks, for feature engineering, model training, and data pipelines.
• Experience with relational databases including Postgres, BigQuery, and MySQL; document databases including MongoDB; and low-latency databases including BigTable.
• Experience building and delivering complex ML solutions from inception to launch.

Responsibilities

• Direct and oversee complex AI initiatives throughout their lifecycle, from formulation and statistical modeling through deployment and scaling.
• Provide technical leadership in developing and optimizing recommendation and decision-engine algorithms, including collaborative filtering, embeddings, clustering, reinforcement learning, and constrained optimization.
• Design and deploy enterprise GenAI solutions, including LLM fine-tuning, RAG pipelines, and prompt engineering; share ownership of governance and engineering standards for these technologies.
• Own AI systems’ non-functional requirements, optimizing for low-latency inference, high throughput, data privacy compliance, and cloud cost efficiency, and help enforce team standards.
• Transition experimental code into modular, version-controlled software packages; implement automated training pipelines, ML CI/CD, and monitoring for data drift and model degradation; establish team practices.
• Define and enforce Enterprise AI engineering standards for unit testing, documentation, peer code reviews, and reproducibility.
• Partner with business and IT product teams to translate use cases into technical requirements, identify risks early, represent AI/ML engineering as a team decision-maker, and serve as a primary engineering-domain contact.
• Evaluate and recommend AI technologies, frameworks, and tools.
• Identify opportunities for AI innovation and advocate for capabilities within an Agile delivery framework.
• Own technical roadmaps and provide detailed engineering time and effort estimates.
• Promote statistical best practices and software engineering principles across the engineering team.
• Present technical results and architectural decisions to senior stakeholders, communicate trade-offs among model complexity, performance, and cost, and serve as a team escalation point and technical authority.

Benefits

• Standard benefits package.
• Eligible associates may earn additional compensation under the company’s bonus plan.
• Paid time off for full-time positions.
• Health, dental, vision, life, and disability benefits for full-time positions.
• Dental, vision, life, and disability benefits for part-time positions.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior