AI Platform Engineer

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.

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.

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.

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 building AI-powered applications, agents, copilots or intelligent automation.

Experience with Retrieval-Augmented Generation, prompt engineering, vector search or enterprise knowledge retrieval.

Experience with major public cloud platforms and associated AI services.

Experience with data platform technologies and governance concepts, including metadata, lineage, cataloguing and access control.

Experience delivering solutions in a regulated or controlled environment.

Experience with MLOps, AI evaluation, monitoring and the operational support of AI solutions.

Knowledge of containerisation, orchestration and API-first architecture.

Familiarity with responsible AI, model governance and AI safety controls.

Delivering secure, scalable platform capabilities used by multiple business teams.

Moving AI use cases into production with appropriate reliability, observability and governance.

Replacing routine manual activity with controlled automation and self-service capabilities.

Improving monitoring and alerting so the team can identify and address issues before users report them.

Providing clear reporting on platform usage, cost, performance, adoption and outcomes.

Increasing organisational capability through reusable assets, documentation and knowledge sharing.

Continuously improving the reliability, efficiency and user experience of our AI and data platforms.