We’re so much more than an energy company. We’re a family of brands revolutionising how we power the planet. We’re energisers. One team of 21,000 colleagues energising a greener, fairer future by creating an energy system that doesn’t rely on fossil fuels, while living our powerful commitment to igniting positive change in our communities. Here, you can find more purpose, more passion, and more potential. That’s why working here is #MoreThanACareer. We do energy differently – we make it, store it, move it, sell it, and mend it.
Centrica, Technology is a core driver of how we deliver our strategy. You’ll join a team modernising our platforms, strengthening cyber and operational resilience, and advancing a product‑led way of working that brings engineers, data specialists and business experts together to deliver meaningful outcomes at pace. We’re scaling automation and AI from proof‑of‑concept into real, end‑to‑end change – improving customer journeys, reducing cost‑to‑serve, accelerating delivery, and building the digital foundations that underpin everything from energy trading and risk to field operations and critical infrastructure. If you want to work on complex, high‑impact problems using modern engineering practices, and help build reusable platforms that will shape how Centrica operates over the next decade, this is the place to do it.
LocationUK‑based hybrid role, occasional travel to site.
Day to day
Design, build and deploy AI and machine learning solutions that deliver measurable customer and business value.
Develop, train and optimise machine learning and generative AI models for use in production systems.
Build and operate scalable data pipelines, model training workflows and inference services using cloud‑native and managed AI platforms.
Collaborate with product managers, engineers and data teams to translate business problems into effective AI solutions.
Own the quality, performance and reliability of AI solutions, including monitoring, retraining and continuous improvement.
Implement responsible AI practices, ensuring solutions meet security, privacy, governance and ethical standards.
Evaluate and select appropriate AI tools, models and platforms, making build‑vs‑buy recommendations where appropriate.
Support live AI services by investigating incidents, analysing model behaviour and resolving production issues.
Continuously explore and apply new AI techniques, frameworks and approaches where they deliver clear benefit.
Take ownership for delivering agreed outcomes, raising risks early and contributing to team delivery and learning.
What We Need From You
Degree in Computer Science, Data Science, Engineering or related discipline, or equivalent practical experience.
Several years in software engineering with at least 2 to 3 years developing AI or machine learning solutions in production environments.
Experience integrating AI models into enterprise platforms and customer‑facing systems.
Strong capability in machine learning frameworks, data modelling and API‑based integration.
Ability to translate business problems into AI solutions. Understanding of data governance, model evaluation and ethical considerations.
Demonstrated experience working as an AI or machine learning engineer delivering models or AI services into production.
Strong experience with modern machine learning and/or generative AI frameworks.
Experience working with large language models, either through fine‑tuning open source models or integrating with managed foundation model platforms.
Hands‑on experience building data pipelines and model workflows using tools such as Python, SQL, Spark or similar data processing technologies.
Experience deploying and operating AI systems in cloud environments using containerisation, managed ML services or serverless architectures.
Understanding of MLOps practices including model versioning, experiment tracking, CI/CD for models and monitoring of model performance and drift.
Experience applying responsible AI principles, including data privacy, bias mitigation, explainability and security controls.
Ability to analyse complex problems, experiment iteratively and translate findings into robust engineering solutions.
Strong collaboration and communication skills, with the ability to work effectively across engineering, product and data teams.
A growth mindset with curiosity for emerging AI technologies and a focus on practical, value‑driven outcomes.
Core Competencies & Technical 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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