Location: UK‑based hybrid role, Occasional travel to site.
Responsibilities
* Plan and coordinate end‑to‑end release cycles – scheduling, scope, dependency mapping, code freeze and blackout windows, release notes, and cutover plans. Run automated pre‑deployment checks (test coverage, quality gates, security scans, approvals) and go/no‑go readiness via pipeline policies. Integrate with Change Enablement (e.g., ServiceNow): auto‑create/update change records, capture evidence, CAB attendance for major releases.
* Engineer, maintain and optimise CI/CD pipelines – Design, build and operate multi‑stage CI/CD pipelines (build, test, security scan, package, deploy) to ensure automated, repeatable and secure release processes that improve deployment reliability and reduce manual effort.
* Manage environment readiness and deployment execution – Define environment topology, variables, secrets, and config, manage ADO Library/Key Vault integrations securely (no secrets in code). Maintain configuration as code, parameterise per environment, and validate via automated smoke tests. Coordinate test data, service virtualisation and infra dependencies to ensure deployments execute successfully and services remain stable.
* Govern release quality, risk and compliance – Apply automated quality gates, security controls, auditing and change‑management processes to all releases, ensuring compliance with organisational standards and reducing change‑related incidents.
* Troubleshoot and resolve release and deployment issues – Identify, analyse and resolve pipeline failures, deployment errors, configuration drift or environment issues, working directly with multidisciplinary teams (MDTs) to minimise downtime and ensure rapid service recovery.
* Drive continuous improvement of release processes – Publish release dashboards (ADO Boards/Power BI) with trend analysis, bottlenecks, and improvement backlogs. Drive post‑release reviews and automation of manual steps, reduce change approval time via evidence‑based risk controls.
Qualifications
* Proven experience in Release Engineering, covering the automated creation, approval, sequencing and deployment of system change packages or in DevOps Engineering, with hands‑on responsibility for delivering complex, multi‑application releases into production environments.
* Experience of building, maintaining and troubleshooting CI/CD pipelines (Azure DevOps, GitHub Actions, Jenkins, or similar), including YAML pipelines, pipeline templates, approvals, gates, and multi‑stage deployment workflows.
* Strong background in release orchestration, including release planning, dependency mapping, deployment execution, release notes, risk assessments and cutover/hyper care activities.
* Experience of integrating automated testing, security scanning, quality gates and compliance checks into CI/CD pipelines (e.g., SonarQube, dependency scans, container image scanning).
* Experience of working with cross‑functional teams (Product Owners, Squad Leads, Development, QE, Security, Architecture, Operations, Change) to coordinate releases, resolve blockers and ensure platform stability.
* Practical experience leading major incident bridges or post‑release issue triage, with the ability to analyse logs, metrics, events and traces to identify root cause and drive remediation.
* Demonstrates the safe and responsible use of AI tools, with clear knowledge of when AI use is appropriate and strong awareness of accuracy, bias and compliance. Bringing the ability to design and reuse prompt templates to support consistent, high‑quality workflow outputs, and skilled in using AI to triage, classify and analyse information within Centrica policy guardrails.
* Strong ability to recognise higher‑risk scenarios and elevate to governance or security as needed. Alongside this, showing proficiency in enterprise AI copilots, knowledge assistants and AI‑enhanced productivity tools.
Core Competencies & Technical Skills (AI and emerging technology)
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 take responsibility for the responsible lifecycle of AI features from experimentation through to deployment and continuous improvement.
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