{ “@context”: “http://schema.org”, “@type”: “JobPosting”, “title”: “Software Engineering Manager – Knowledge/AI and Platform Enablement Squads”, “description”: “
Location: UK-based hybrid role, occasional travel to site.
Day to day
- Provide senior technical leadership across Knowledge/AI and Platform Enablement squads – define standards, patterns and set technical direction, ensuring the platform is scalable, resilient and aligned with Centrica’s technology strategy.
- Own the end‑to‑end software development life cycle across multiple squads – ensure teams follow robust engineering practices throughout discovery, design, build, testing, deployment and operations, consistently meeting business and non‑functional requirements.
- Guide complex design decisions and technical solutioning – lead technical reviews, challenge design options, and ensure solutions follow best practice, reduce duplication, and promote maintainable, secure and performant systems.
- Enhance delivery capability through platform reliability and DevOps maturity – continuously improve deployment pipelines, observability, alerting, incident handling, recovery procedures and operational readiness.
- Manage stakeholders and ensure transparent communications – build strong relationships across product, operations, delivery and business teams to coordinate plans, manage risk, align on priorities and ensure clarity of expectations.
- Drive engineering excellence and uplift team capability – track, analyse and improve engineering excellence metrics; coach engineers and promote a culture of learning, continuous improvement, and high‑quality engineering.
- Support data‑driven and API‑centric integration design – ensure APIs, integrations and data flows are designed with consistent patterns, strong data quality, security, scalability and operational resilience.
- Improve how work is done – identify opportunities to simplify processes, reduce technical debt, automate repetitive tasks, use AI‑assisted engineering, and enhance developer experience.
What we need from you
- Degree in Computer Science / Engineering or equivalent professional experience.
- Proven experience shaping and delivering technology strategy in a complex engineering environment.
- Strong hands‑on experience in at least one core programming language.
- Hands‑on experience managing Salesforce teams and implementation of standardised patterns.
- Expertise in API management, integration patterns, event‑driven architectures, AI models and microservices.
- Strong understanding of data management, data modelling and data quality controls.
- Ability to produce high‑level and detailed design specifications.
- Experience running DevOps practices including CI/CD, observability, monitoring and incident management.
- Multi‑squad engineering leadership.
- Proven experience leading software engineering delivery in a complex, multi‑team environment.
- Experience providing technical leadership and governance across the full SDLC.
- Experience working in agile, product‑led delivery environments.
Mindset & Ways of Working
- Comfortable working in iterative, outcome‑focused, agile environments.
- Open to new tools, automation and AI‑assisted engineering approaches.
- Highly collaborative across product, engineering, design and operations.
- Strong problem solver with a simplification and continuous improvement mindset.
- Senior technical decision‑making – strong technical background in modern software engineering practices (APIs, integration, CI/CD, DevOps).
- Ability to guide architectural and design decisions and manage non‑functional requirements.
- Strong stakeholder management and communication skills.
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; preparing and managing data used in AI workflows and taking responsibility for the responsible lifecycle of AI features from experimentation through to deployment and continuous improvement.
Company: Centrica
Location: Windsor
Job Description:
Location: UK-based hybrid role, occasional travel to site.
Day to day
- Provide senior technical leadership across Knowledge/AI and Platform Enablement squads – define standards, patterns and set technical direction, ensuring the platform is scalable, resilient and aligned with Centrica’s technology strategy.
- Own the end‑to‑end software development life cycle across multiple squads – ensure teams follow robust engineering practices throughout discovery, design, build, testing, deployment and operations, consistently meeting business and non‑functional requirements.
- Guide complex design decisions and technical solutioning – lead technical reviews, challenge design options, and ensure solutions follow best practice, reduce duplication, and promote maintainable, secure and performant systems.
- Enhance delivery capability through platform reliability and DevOps maturity – continuously improve deployment pipelines, observability, alerting, incident handling, recovery procedures and operational readiness.
- Manage stakeholders and ensure transparent communications – build strong relationships across product, operations, delivery and business teams to coordinate plans, manage risk, align on priorities and ensure clarity of expectations.
- Drive engineering excellence and uplift team capability – track, analyse and improve engineering excellence metrics; coach engineers and promote a culture of learning, continuous improvement, and high‑quality engineering.
- Support data‑driven and API‑centric integration design – ensure APIs, integrations and data flows are designed with consistent patterns, strong data quality, security, scalability and operational resilience.
- Improve how work is done – identify opportunities to simplify processes, reduce technical debt, automate repetitive tasks, use AI‑assisted engineering, and enhance developer experience.
What we need from you
- Degree in Computer Science / Engineering or equivalent professional experience.
- Proven experience shaping and delivering technology strategy in a complex engineering environment.
- Strong hands‑on experience in at least one core programming language.
- Hands‑on experience managing Salesforce teams and implementation of standardised patterns.
- Expertise in API management, integration patterns, event‑driven architectures, AI models and microservices.
- Strong understanding of data management, data modelling and data quality controls.
- Ability to produce high‑level and detailed design specifications.
- Experience running DevOps practices including CI/CD, observability, monitoring and incident management.
- Multi‑squad engineering leadership.
- Proven experience leading software engineering delivery in a complex, multi‑team environment.
- Experience providing technical leadership and governance across the full SDLC.
- Experience working in agile, product‑led delivery environments.
Mindset & Ways of Working
- Comfortable working in iterative, outcome‑focused, agile environments.
- Open to new tools, automation and AI‑assisted engineering approaches.
- Highly collaborative across product, engineering, design and operations.
- Strong problem solver with a simplification and continuous improvement mindset.
- Senior technical decision‑making – strong technical background in modern software engineering practices (APIs, integration, CI/CD, DevOps).
- Ability to guide architectural and design decisions and manage non‑functional requirements.
- Strong stakeholder management and communication skills.
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; preparing and managing data used in AI workflows and taking responsibility for the responsible lifecycle of AI features from experimentation through to deployment and continuous improvement.
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Posted: May 21st, 2026