Campus – Full Time – AI Engineer – 2027 (UK – Burgess Hill)

Overview

As an AI Engineer I in Enterprise Technology Services, you join a graduate program to help design, test, and scale AI-enabled solutions. You will work across ML, LLM integrations, data pipelines, and intelligent services, collaborating with cross-functional partners to deliver responsible, reliable AI capabilities. You’ll gain hands-on experience while contributing to governance, security, and quality of AI initiatives. This role offers exposure to enterprise-scale AI and a path to impact across teams and products.

Responsibilities

• Support development, testing, and integration of AI/ML models, LLM integrations, and data pipelines under guidance
• Assist data collection, preprocessing, transformation, and validation for model training and evaluation
• Contribute to debugging and improving AI-enabled solutions for performance, reliability, explainability, and maintainability
• Support AI workflows including model training, inference endpoints, and prompt-based interactions
• Collaborate with engineering, product, data, risk, security, and business partners to implement AI-driven solutions
• Document model parameters, prompts, data pipelines, integrations, and technical decisions for reproducibility
• Participate in Agile practices and team ceremonies
• Assist in ensuring AI systems align with reliability, safety, governance, security, and compliance

Key requirements

• Bachelor’s or Master’s degree in a technical field completed before full-time start date
• Knowledge of Python and foundational data processing technologies
• Foundational CS concepts (data structures, algorithms, OOP, debugging, testing)
• Foundational ML concepts (supervised/unsupervised learning, feature engineering, experimentation)
• Introductory understanding of modern AI systems and LLM APIs
• Ability to support AI/ML development, testing, documentation, and data pipelines under guidance
• Awareness of responsible AI (reliability, safety, governance, security, privacy, escalation)
• Strong communication, collaboration, documentation, and learning agility

• communication
• collaboration
• learning agility
• Python
• data processing
• machine learning concepts

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