At M&G, our purpose is to give everyone real confidence to put their money to work. With a heritage dating back more than 175 years, we have a long history of innovation in savings and investments, combining asset management and insurance expertise to offer a wide range of solutions.

Our two distinct operating segments, Asset Management and Life, work together to provide access to balanced, long-term investment and savings solutions.

Through telling it like it is, owning it now and moving it forward together with care and integrity; we are creating an exceptional place to work for exceptional talent.

We will consider flexible working arrangements for any of our roles and offer workplace adjustments to ensure you have the support you need to succeed in your role.

The AI and Data Platforms team designs, builds and runs the platforms that enable our organisation to adopt AI and data capabilities. We own the full platform lifecycle, from architecture and engineering through to operations and continuous improvement.

As an AI Platform Engineer, you'll extend our platform capabilities, embed AI and automation into platform operations, and help teams across the business adopt AI solutions safely and effectively.

This is a hands-on engineering role that combines feature delivery with responsibility for resilience, observability, governance and measurable outcomes. You'll work closely with Product Owners, Architects, Engineers, Security teams and business stakeholders to deliver secure, scalable and reliable platform capabilities.

Main Responsibilities

Platform delivery

• Design, build and deliver new platform capabilities across a multi-cloud environment.
• Contribute to solution design, technical decisions and implementation.
• Build production-ready services, APIs, automation and platform components.
• Apply modern engineering practices, including automated testing, CI/CD, infrastructure as code, security by design and observability.
• Take platform capabilities from initial design through to production use and ongoing improvement.

AI-driven platform operations

• Apply AI and agentic capabilities to improve platform operations and engineering workflows.
• Build automation and agents that support environment provisioning, onboarding, access management, fault diagnosis and remediation.
• Automate the operational lifecycle of environments, workspaces, resources, agents and permissions.
• Develop self-service capabilities that reduce manual effort while maintaining appropriate controls.
• Evaluate emerging AI capabilities and recommend practical approaches to adoption.

Resilience, reliability and service quality

• Design solutions that remain reliable and predictable when failures occur.
• Build for known failure modes using appropriate retry, isolation, recovery and service degradation patterns.
• Test recovery processes and use the results to strengthen platform resilience.
• Implement monitoring, alerting, telemetry and operational dashboards.
• Define and track service measures that reflect user needs and platform performance.
• Investigate and resolve operational issues, continuously improving platform reliability and user experience.

Measurement and value

• Build reporting, telemetry and analytics that provide clear visibility of platform usage, cost, performance and outcomes.
• Support the definition and tracking of KPIs, OKRs and service measures.
• Develop reporting that demonstrates adoption and business value to technical stakeholders and senior audiences.
• Use operational and user data to guide platform decisions and continuous improvement.

Enablement and adoption

• Work with engineering, data and business teams to design, integrate and support AI use cases.
• Help teams move AI use cases into secure and reliable production environments.
• Develop reusable patterns, standards, documentation and self-service capabilities.
• S upport user onboarding, enablement and go-live activities.
• Share knowledge and promote consistent engineering practices across teams.
• Gather feedback and use it to improve the platform and user experience.

Governance, security and responsible AI

• Make sure platform capabilities align with enterprise security, governance, privacy and risk requirements.
• Support the explainability, auditability and transparency of AI-enabled capabilities.
• Apply appropriate controls and guardrails throughout the AI lifecycle.
• Work with data governance, cataloguing, lineage and access-management capabilities where required.
• Contribute to a strong control environment and support the responsible adoption of AI.

Key Knowledge, Skills and Experience

Essential

• Demonstrable experience building and operating cloud-based platforms and services.
• Strong understanding of cloud fundamentals, including networking, identity and access management, security and operational controls.

• Strong software engineering or scripting capability, with experience working in production codebases.
• Experience using infrastructure as code, CI/CD and automated delivery practices.
• Experience developing and supporting cloud-native applications, APIs, services or platforms.
• Experience using logs, metrics, traces and telemetry to improve platform performance and reliability.
• Experience designing solutions with resilience, scalability, observability and operability in mind.
• Evidence of improving the dependability of production systems through practical engineering changes.
• A track record of delivering technology capabilities into production and supporting their ongoing operation.
• Ability to communicate complex technical concepts clearly to technical and non-technical audiences.
• A collaborative approach to working with engineering, data, security and business stakeholders.
• An interest in AI, automation and agentic systems, combined with sound judgement about where they add value.

D esirabl e

Experience b