About the job
About Charlotte Tilbury Beauty
Established in 2013 by the renowned British makeup artist and beauty innovator Charlotte Tilbury MBE, Charlotte Tilbury Beauty has transformed the global beauty landscape by simplifying makeup applications to be accessible for everyone, everywhere. Our diverse and easily navigable product range has made gifting effortless and enjoyable.
In just a decade, Charlotte Tilbury Beauty has achieved remarkable growth and has become one of the most discussed brands in the beauty sector and beyond. With a presence in over 50 markets and a rapidly expanding team of more than 2,300 employees globally, we pride ourselves on being part of the Dream Team that brings our magic to life.
Today, we are a truly global enterprise, driving market-leading growth through innovative retail strategies and product launches powered by cutting-edge technology. Our culture fosters a spirit of challenge, creativity, and collaboration, and we are always on the lookout for extraordinary talent to join us in our journey towards limitless ambitions.
About the Role
As part of the AI & ML Engineering team, you will play a crucial role in advancing the integration of AI technologies across the organization. This position is focused on fostering innovation while ensuring our machine learning products are robust, scalable, and cost-effective. You will empower teams to address challenges by utilizing existing AI tools and developing custom solutions when necessary. Your responsibilities will encompass AI enablement, the development of agentic systems, and conventional ML engineering for non-GenAI applications such as recommender systems. This hands-on engineering role is highly visible and emphasizes pragmatic, production-ready solutions that yield tangible business results. You will collaborate closely with the wider Data team (including Data Science and Core Data Engineering) as well as other departments (Technology, Legal, Information Security) to onboard, develop, and scale AI applications.
Key Responsibilities:
- Partner with stakeholders to define problems and identify optimal solutions, whether through existing AI tools or by creating tailored workflows & solutions.
- Design and implement agentic systems utilizing techniques such as RAG, grounding, prompt engineering, and orchestration on a GCP-first stack.
- Develop and maintain production ML pipelines and services for non-GenAI use cases (e.g., recommender systems, customer segmentation models, marketing optimization modules) using supervised, unsupervised, and econometric modeling techniques.