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Software engineering manager - knowledge/ai and platform enablement squads

Windsor
Centrica
Software engineering manager
€85,000 a year
Posted: 20 May
Offer 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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