Software Engineering Manager - In-Life Domain

Company: Centrica

Location: Windsor

Posted: May 20th, 2026

Overview

We’re so much more than an energy company. We’re a family of brands revolutionising how we power the planet. One team of 21,000 colleagues energises a greener, fairer future by creating an energy system that doesn’t rely on fossil fuels while igniting positive change in our communities. Central to our strategy, technology modernises platforms, strengthens cyber and operational resilience, and advances a product‑led way of working that brings engineers, data specialists and business experts together to deliver meaningful outcomes at pace.

The Software Engineering Manager provides senior technical leadership, engineering governance, and delivery oversight for the Field Operations Platform. The role ensures platform features, integrations and non‑functional capabilities are engineered to a high standard, supporting accurate forecasting, workforce planning and safe, efficient customer service delivery. It leverages automation, data and AI‑enabled tooling to reduce manual effort, enhance engineering quality, improve cycle times and increase reliability.

Location: UK‑based hybrid role, occasional travel to site.

Responsibilities

Qualifications

Work Experience & Technical Skills

Mindset & Ways of Working

Core Competencies & Technical Skills (AI and Emerging Technology)

Designs, integrates and operates AI‑enabled solutions within enterprise environments, including prompt‑driven workflows, retrieval‑augmented systems and AI agents. Applies structured evaluation, testing and monitoring practices to ensure AI outputs are reliable, secure and compliant with organisational guardrails. Prepares and manages data used in AI workflows and takes responsibility for the responsible lifecycle of AI features from experimentation through deployment and continuous improvement.

Core Behaviours

AI / Digital Fluency Skills

Ability to design, integrate and operate AI enabled solutions within enterprise environments, including prompt driven workflows, retrieval augmented systems and AI agents. Applying structured evaluation, testing and monitoring practices to ensure AI outputs are reliable, secure and compliant with organisational guardrails. Prepares and manages data used in AI workflows and takes responsibility for the responsible lifecycle of AI features from experimentation through to deployment and continuous improvement.

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