Professional Services Intern – Global Customer Services (fall 2026)

Cloudflare Cloudflare · Enterprise · London, United Kingdom · University

Intern role focused on building AI-driven prototypes using LLMs and RAG to automate workflows within Professional Services, including project and resourcing optimization, forecasting, and data preparation for model training. The role also involves refining prompts, translating business needs into technical requirements, and contributing to documentation.

What you'd actually do

  1. Design, develop, and deploy AI-driven prototypes (using LLMs, RAG architectures, or predictive modeling) to automate manual workflows within the Professional Services Project and Resourcing ecosystem. Build out models to help automate future forecasting and resource planning.
  2. Partner with our engineering teams to identify, clean, and structure high-volume post-sales data for model training and fine-tuning to enable faster resourcing capabilities.
  3. Refine and optimize system prompts and model parameters to ensure high-quality, hallucination-free outputs for internal advisory tools.
  4. Act as the technical translator, taking high-level optimization ideas from GCS leadership and turning them into technical requirements and minimum viable products (MVPs).
  5. Contribute to internal knowledge bases and documentation to improve team efficiency and consistency. Create collateral to enable the team.

Skills

Required

  • Currently pursuing a Bachelor’s or Master’s degree in Business, Project Management or a related technical field with an element of AI.
  • Proficiency in verbal, written, and visual communication
  • Working seamlessly with diverse teams
  • The ability to identify bottlenecks, analyse root causes, and propose practical solutions
  • Pivot gracefully when scopes change or unforeseen challenges

Nice to have

  • AI-native curiosity

What the JD emphasized

  • AI-driven prototypes
  • LLMs
  • RAG architectures
  • predictive modeling
  • automate manual workflows
  • model training and fine-tuning
  • system prompts and model parameters
  • hallucination-free outputs

Other signals

  • AI-driven prototypes
  • LLMs
  • RAG architectures
  • predictive modeling
  • automate manual workflows
  • model training and fine-tuning
  • system prompts and model parameters
  • hallucination-free outputs