Lead Architect for a national award-winning AI and machine learning cloud research platform — 30+ data sources integrated, a stalled migration revived and driven to 80% cloud-shift in six months.
Research computing in higher education sits at an uncomfortable intersection: the scientific ambition of researchers, the data governance requirements of universities, and the compliance obligations of research funders rarely point in the same direction. This major UK university was attempting to build a national-scale AI and machine learning research platform — an initiative that would require integrating data from more than 30 sources, including external research partners, public datasets, and sensitive institutional data.
By the time Magma Cloud was brought in, the migration was stalled. Technical decisions had been made without sufficient architectural oversight, integration complexity had not been fully scoped, and the programme had lost momentum. Research activities that were meant to be running in the cloud were still on ageing on-premises infrastructure. The window for recovery was limited — the platform needed to be operational to support nationally significant research programmes, and the reputational stakes were high.
The organisation needed senior architectural leadership capable of rapidly diagnosing what had gone wrong, restructuring the approach, and driving delivery at pace — without cutting corners on security or data governance.
Magma Cloud stepped in as Lead Architect, taking responsibility for the technical direction of the programme from the point of recovery through to production deployment.
The first step was rapid diagnosis: reviewing what had been built, what had been decided, and where the gaps lay. Within the first weeks, we had a clear picture of the issues — and a restructured technical approach that could be delivered in the time available.
We then led the design and delivery of the cloud research platform architecture, integrating more than 30 data sources into a unified, governed research environment. This involved working with data owners across the university and external partners to establish data sharing agreements, classification standards, and access controls that satisfied both research requirements and governance obligations.
Security was embedded throughout: the platform was designed with least-privilege access, data classification, audit logging, and research data segregation from day one. Sensitive research datasets were handled with controls appropriate to their classification, without creating barriers that would impede legitimate research activity.
The stalled migration was restructured into a sequenced delivery plan — prioritising the workloads with the greatest research impact and moving at pace. Within six months of taking over architectural leadership, the platform had achieved an 80% cloud-shift, with the most critical research workloads running in the cloud and delivering the performance researchers needed.
The platform went on to receive national recognition, winning an award in its sector — a direct outcome of the quality of its architecture and the pace at which it was delivered.
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