AI & ML Engineer

About Charlotte Tilbury Beauty

Founded by British makeup artist and beauty entrepreneur Charlotte Tilbury MBE in 2013, Charlotte Tilbury Beauty has revolutionised the face of the global beauty industry by de-coding makeup applications for everyone, everywhere, with an easy-to-use, easy-to-choose, easy-to-gift range. Today, Charlotte Tilbury Beauty continues to break records across countries, channels, and categories and to scale r the last 10 years, Charlotte Tilbury Beauty has experienced exceptional growth and is one of the most talked about brands in the beauty industry and beyond.

It has become a global sensation across 50 markets (and growing), with over 2,300 employees globally who are part of the Dream Team making the magic happen. Today, Charlotte Tilbury Beauty is a truly global business, delivering market-leading growth, innovative retail and product launches fuelled by industry-leading tech — all with an internal culture of embracing challenges, disruptive thinking, winning together, and sharing the magic.

The energy behind the brand is infectious, and as we grow, we are always looking for extraordinary talent who want to be part of this our success and help drive our limitless ambitions.

About the role

The AI & ML Engineering team accelerates the adoption of AI across the business, championing innovation while ensuring our machine learning products are robust, scalable, and cost-efficient. We enable teams to solve problems using existing AI tools where possible and build custom solutions when needed. Our remit spans AI enablement, agentic systems development, and “conventional” ML engineering for non-GenAI applications like recommender systems.

This is a highly visible, hands-on engineering role with a strong focus on pragmatic, production-ready solutions that drive real business impact. This role and team collaborate closely with the wider Data team (Data Science, Core Data Engineering) and the wider organisation (Technology, Legal, Information Security) to onboard/develop & scale AI applications.

As AI & ML Engineer you will be responsible for

The role covers the full AI/ML engineering lifecycle, from discovery to deployment and monitoring. Responsibilities include

• Partnering with stakeholders to scope problems and identify the right solution - whether leveraging existing AI tools or building custom workflows & solutions.
• Designing and implementing agentic systems using techniques spanning RAG, grounding, prompt engineering, and orchestration on a GCP-first stack.
• Building and maintaining production ML pipelines and services for non-GenAI use cases (e.g. recommender systems, customer segmentation models, marketing optimisation modules, leveraging supervised, unsupervised and/or econometric modelling approaches).
• Developing APIs and microservices for AI/ML solutions, ensuring security, scalability, and observability.
• Implementing CI/CD for ML services, writing infrastructure as code, and monitoring for model/data drift and performance.
• Establishing robust guardrails for safe AI usage, including prompt security, practical evaluation frameworks, and compliance with privacy regulations.
• Driving and evangelizing best practices, reusable templates, and documentation to scale AI/ML delivery across the business.
• Collaborating with data engineers, data scientists, front & back-end engineers, product managers, legal & infosec colleagues to deliver impactful solutions end-to-end.

Who you will work with

The AI & ML Engineer Lead and the wider data team.

About you

The role requires a blend of technical depth and product sense, including

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
• Strong Python engineering skills (FastAPI, testing, typing) and experience with cloud-native development (GCP preferred).
• Hands-on experience with GCP Vertex AI (model endpoints, pipelines, embeddings, vector search) or equivalent cloud-native ML platforms (e.g. AWS Sage Maker, Azure ML) and agent orchestration frameworks such as Lang Chain and Lang Graph
• Solid understanding of MLOps - CI/CD, IaC (Terraform), experiment tracking, model registry, and monitoring.
• Proven experience deploying and…