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Ai Engineering Manager

Job in Dartford, Kent County, DA1, England, UK
Listing for: Laing O'Rourke
Full Time position
Listed on 2026-06-08
Job specializations:
  • IT/Tech
    AI Engineer, Data Science Manager
Salary/Wage Range or Industry Benchmark: 80000 - 100000 GBP Yearly GBP 80000.00 100000.00 YEAR
Job Description & How to Apply Below
Position: AI ENGINEERING MANAGER

AI Engineering Manager

Introduction

The IT function has renewed its strategy in response to Laing O'Rourke's ambition to help transform an industry, making it more sustainable, more productive, and fit for the future.

Our opportunity is to apply technology in ways that genuinely matter, shaping how complex projects are delivered, how decisions are made, and how innovation improves outcomes for people, communities, and the environment.

The mission is clear - to create a modern, resilient technology environment, where data underpins every decision, AI enhances every process, and digital capability accelerates progress at scale.

We are building a different kind of IT function to help achieve this, one that is trusted, forward-looking, and deeply connected to the success of the business.

You will work on meaningful technical challenges, contribute to important initiatives, and grow your capability in a supportive environment. You will be trusted with responsibility, encouraged to contribute ideas, and able to see the impact of your work.

We are looking for people who are curious, thoughtful and motivated by contributing to something larger than themselves.

Role Purpose

The AI Engineering Manager is responsible for the design, development, and scaling of production-grade AI and machine learning solutions across Laing O'Rourke.

Reporting to the Principal Lead – Data & AI Solutions and Insight, this role leads a multidisciplinary team to deliver AI capabilities that automate processes, enhance decision-making, and unlock measurable business value.

You will ensure AI solutions are engineered, deployed, and scaled effectively, working as part of the wider Data & AI operating model:

  • Enterprise Data & AI Enablement – defines where and how data and AI are applied across the business
  • Data & AI Solutions and Insight – builds and delivers the solutions that realise that value
  • Data Platforms and Governance – provides trusted, secure, and scalable data foundations

You will be accountable for moving AI from experimentation to reliable, enterprise-scale capability, ensuring solutions are robust, secure, and embedded into operational systems.

Key Accountabilities

AI Solution Engineering and Delivery

  • Lead the design, development, and deployment of AI and machine learning solutions into production
  • Ensure solutions are scalable, reliable, and aligned to enterprise architecture and data platforms
  • Drive the transition from experimentation to production and sustained operation

Use Case Delivery and Business Value

  • Partner with the Data & AI Enablement team and business stakeholders to deliver high-value AI use cases
  • Translate business challenges into practical, outcome-driven AI solutions
  • Ensure clear linkage between AI delivery and measurable business outcomes

Engineering Excellence and Standards

  • Establish best practices for model development, testing, deployment, and monitoring
  • Implement strong MLOps and engineering discipline across AI delivery
  • Promote reuse of models, components, and delivery patterns

Integration with Data Platforms

  • Work closely with the Data Platforms and Governance team to ensure AI solutions are built on trusted, well-managed data
  • Ensure efficient data access, integration, and performance for AI workloads

Governance and Responsible AI

  • Ensure AI solutions comply with enterprise governance, security, and data standards
  • Embed responsible AI practices including transparency, explainability, and ethical use
  • Monitor model performance, drift, and business impact

Scaling and Operationalisation

  • Ensure AI solutions are embedded into business processes and operational systems
  • Drive reuse and scalability across use cases and domains
  • Reduce reliance on bespoke solutions through standardisation and platform use

Leadership Contribution

  • Build and lead a high-performing AI engineering team
  • Develop capability in machine learning, engineering, and delivery discipline
  • Foster a culture of quality, accountability, and continuous improvement
  • Act as a visible and credible technical leader within the IT team

Key Measures of Success

Success in the role will be demonstrated through:

  • Successful delivery of production-grade AI solutions with measurable business…
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