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# Sr AI ML Engineer

- **Company:** ULTA Salon, Cosmetics & Fragrance, Inc.
- **Location:** Bolingbrook, IL, United States (Remote available)
- **Experience:** Expert
- **Salary:** $165,984.0 - $166,984.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Algorithm Design, Amazon Web Services, Unit Testing, Microsoft Azure, Big Data, BigTable, BigQuery, C++ (Programming Language), Cloud Engineering, Code Review, Databases, Continuous Integration, Relational Databases, Decision Support Systems, Python (Programming Language), PostgreSQL, Machine Learning, MongoDB, MySQL, Object-Oriented Software Development, Recommender Systems, Tensorflow, Software Engineering, Systems Integration, Reinforcement Learning, Google Cloud, Enterprise Software Applications, Feature Engineering, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Apache Spark, Event Driven Architecture, Scikit Learn, Information Technology, Low Latency, Optimization Algorithms, Machine Learning Operations, Cloud Optimization, Artificial Intelligence Markup Language (AIML), Data Pipelines, Docker, Unsupervised Learning, Databricks
- **Published:** May 19, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=c97a6763a721f8de

## About the Role

Master's degree in Computer Science, Mathematics, Physics, Statistics or other quantitative field. Three (3) years in any occupation with experience working in data science, machine learning or AI.Three (3) years in any occupation with experience working in data science, machine learning or AI must include: Three (3) years of experience working in the Retail Industry. Three (3) years of experience writing production-quality object-oriented code such as Python or C++ and working with ML frameworks such as scikit-learn, TensorFlow, or similar, including three (3) years of experience building low-latency or real-time ML systems. Three (3) years of experience designing, building, and deploying large-scale recommender or decisioning systems, including techniques such as ranking, clustering, and other unsupervised learning methods. Three (3) years of experience applying optimization techniques in production environments, including large-scale ML or decisioning systems such
as operational research, offer optimization, or revenue maximization. Three (3) years of experience building and deploying production ML systems on cloud platforms such as Google Cloud, Azure or AWS. Three (3) years of experience building scalable, production-grade real-time inference services using Docker, with CI/CD and monitoring. Two (2) years 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. One (1) year of experience designing and developing reinforcement learning models in real-world or production settings. One (1) year of experience working with large language models (LLMs). Experience must also include: Experience working 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 lowlatency databases including BigTable; and Experience building and delivering complex ML solutions from inception to launch.

TELECOMMUTING EMPLOYEE: Reports to company headquarters in Bolingbrook, IL. Can work remotely or telecommute up to 100%.

## Description

Lead the full lifecycle execution of complex AI initiatives, bridging the gap between exploratory data science and production engineering, including formulation and statistical modeling to deployment and scaling. 20.0%

Develop and optimize advanced algorithms for recommendation and decision engines, utilizing techniques such as collaborative filtering, embeddings, clustering, reinforcement learning, and constrained optimization. 15.0%

Design pipelines for Large Language Models (LLMs) including fine-tuning, RAG (Retrieval-Augmented Generation), and prompt engineering for enterprise retail use cases like analytics augmentation and decision support. 15.0%

Take ownership of the non-functional requirements of AI systems, explicitly optimizing models for low-latency inference, high throughput, data privacy compliance, and cloud cost efficiency. 10.0%

Transition code from experimental notebooks to modular, version-controlled software packages. Implement automated training pipelines, CI/CD for ML, and robust monitoring for data drift and model degradation. 10.0%

Contribute to and enforce engineering standards within the Enterprise AI team, including unit testing, documentation, peer code reviews, and reproducibility frameworks. 5.0%

Partner with business and IT product teams to refine vague use cases into concrete engineering requirements, identifying technical risks early in the project planning process. 5.0%

Map out detailed technical roadmaps, providing accurate time and effort estimates for both the research (Data Science) and implementation (Engineering) phases of projects. 5.0%

Mentor junior team members and peers on both statistical best practices and software engineering principles, fostering a team culture of technical excellence. 10.0%

Present technical results and architectural decisions to senior stakeholders, effectively communicating the trade-offs between model complexity, performance, and cost. 5.0%


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