Overview

We are working towards our net-zero emissions target by 2030, transitioning our global fleet to more sustainable technologies such as electric vehicles, hybrid systems, and Stage V generators.

A critical part of this transformation is improving fleet management.

We are looking for an AI Engineer to design and deliver a pilot AI solution that automates fleet diagnostics and enables data-driven decision-making, leveraging modern AI and Large Language Model (LLM) capabilities.

Responsibilities

You will lead the end-to-end development of a pilot AI solution, including:

Discovery & Requirements

• Engage with operations, fleet management, and data stakeholders
• Identify automation opportunities across fleet workflows
• Define functional and technical requirements

Data Exploration & Preparation

• Identify relevant data sources (telemetry, maintenance records, asset data, operational logs)
• Extract and transform data using SQL and data pipelines
• Clean and structure datasets for AI/ML use

AI Solution Design

• Design scalable architecture using Azure and/or AWS
• Evaluate and integrate LLMs where appropriate
• Define APIs and services for model interaction

Prototype Development

• Build AI services using Python
• Develop data pipelines and model workflows
• Create APIs to integrate with internal systems
• Deliver a working prototype for fleet condition diagnostics

Pilot Demonstration

• Present the solution to stakeholders
• Produce technical documentation
• Provide recommendations for scaling to production

Key Deliverables

• Requirements & Discovery Report
• Data Inventory and Data Quality Assessment
• AI Architecture Design (including models, APIs, and cloud services)
• Fleet Diagnostics AI Prototype
• Technical Documentation
• Pilot Demonstration and Recommendations

Required Skills & Experience

• Strong Python development experience
• SQL for data extraction and transformation
• Experience with Azure and/or AWS
• Experience working with Large Language Models (LLMs)
• API development and system integration experience
• End-to-end AI/ML solution architecture experience

Success Criteria

• Delivery of a working AI pilot
• Automated generation of fleet condition insights from available data
• Scalable architecture validated for future production deployment
• Positive stakeholder validation of the solution’s value

Working Arrangement

• We will provide access to stakeholders, data, and cloud environments
• The AI Engineer will take ownership of discovery, design, development, and demonstration

Additional Information

This is a high-impact opportunity to apply AI within a global organisation, working on a tangible use case with clear operational and sustainability outcomes.