Charlotte Tilbury Beauty

AI/ML Engineer

Charlotte Tilbury Beauty

full-time

Posted on:

Origin:  • 🇬🇧 United Kingdom

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Job Level

Mid-LevelSenior

Tech Stack

AWSAzureCloudGoogle Cloud PlatformMicroservicesPythonTerraform

About the role

  • Partnering with stakeholders to scope problems and identify the right solution
  • Designing and implementing agentic systems using RAG, grounding, prompt engineering, and orchestration on a GCP-first stack
  • Building and maintaining production ML pipelines and services for non-GenAI use cases (recommender systems, customer segmentation, marketing optimisation)
  • 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 privacy compliance
  • Driving and evangelizing best practices, reusable templates, and documentation to scale AI/ML delivery
  • Collaborating with data engineers, data scientists, front & back-end engineers, product managers, legal & infosec colleagues to deliver end-to-end solutions

Requirements

  • Bachelor’s or Master’s degree in Computer Science, Engineering, or related field
  • Strong Python engineering skills (FastAPI, testing, typing)
  • Experience with cloud-native development (GCP preferred)
  • Hands-on experience with GCP Vertex AI or equivalent cloud-native ML platforms (AWS SageMaker, Azure ML)
  • Experience with agent orchestration frameworks such as LangChain and LangGraph
  • Solid understanding of MLOps: CI/CD, IaC (Terraform), experiment tracking, model registry, and monitoring
  • Proven experience deploying and operating ML systems in production (batch and real-time)
  • Familiarity with RAG architectures, prompt engineering, and evaluation techniques
  • Strong grasp of security, privacy, and governance principles (IAM, secrets, PII handling)
  • Excellent communication skills and ability to work with non-technical stakeholders
  • (Nice to have) Experience with vector databases and retrieval strategies
  • (Nice to have) Knowledge of recommender systems and ranking models
  • (Nice to have) Familiarity with LLM evaluation tools (e.g., RAGAS, TruLens, LangSmith, Arize)
  • (Nice to have) Exposure to feature stores, data lineage, and observability stacks
  • (Nice to have) Experience in e-commerce or retail environments
  • (Nice to have) Demonstrable ability to weigh up build/build/configure decisions in the LLM space