{ “@context”: “http://schema.org”, “@type”: “JobPosting”, “title”: “AI Software Engineer”, “description”: “
Location: UK-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.
Company: Centrica
Location: Clewer Village
Job Description:
Location: UK-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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Posted: May 10th, 2026