DevOps, cloud operations and platform reliability Build, maintain and improve automation for cloud and IoT platform services that support connected products. Develop and maintain CI/CD pipelines, deployment workflows, environment controls and repeatable release processes using tools such as GitHub, Azure DevOps and related platform tooling. Help maintain Azure-based development, test, QA and production environments used by digital, IoT, data and ML platforms. Contribute to secure platform configuration, access control, secrets management, monitoring and operational governance. Monitor platform health, investigate operational issues and contribute to improving reliability, supportability and repeatability across digital product environments. IoT data flows, data pipelines and operational support Support reliable device-to-cloud data flows from connected products, gateways, cloud services and downstream data platforms. Work with telemetry, time-series and industrial IoT data sources, helping to ensure data is available, structured and usable for engineering, product and analytics teams. Support operational data pipelines that provide reliable data for engineering analytics, product insights, connected services and machine learning use cases. Assist with troubleshooting data ingestion, connectivity, data quality and integration issues across the connected-products ecosystem. Document deployment steps, support processes and platform knowledge to improve maintainability and reduce reliance on manual intervention. Collaboration, security and controlled platform change Work with IT, cyber security, software, firmware and supplier teams to support service availability, incident resolution and controlled platform change. Contribute to data validation, monitoring, transformation and handover between operational platforms and analytical environments. Proactively identify opportunities to simplify, automate and strengthen digital platform delivery. Growth into MLOps and ML platform engineering Develop capability in MLOps practices such as experiment tracking, model packaging, model registry, model promotion and governed deployment workflows. Support AI developers with repeatable workflows that move ML code, configuration and artefacts through controlled development, test, QA and production stages. Contribute to the longer-term development of the ML platform, including quality gates, lifecycle controls, operational monitoring and supportability.