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Data Architect

The University of Edinburgh - College of Science & Engineering - EPCC

Location: Edinburgh
Salary: £48,350 to £59,421 per annum
Hours: Full Time
Contract Type: Permanent
Placed On: 20th September 2024
Closes: 17th October 2024
Job Ref: 10191

Full time  - 35 hours per week

Contract type - Open ended (permanent)

EPCC at the University of Edinburgh is the UK’s leading centre for Supercomputing and Data Analytics. We are seeking a Data Architect to work as technical lead in maximising the capabilities of the Edinburgh International Data Facility (our AI and Data Science infrastructure) to ensure the provision aligns with the requirements of our high profile academic, public sector and commercial stakeholders. 

From day one you will be working to improve a platform already used for some of today’s most exciting, challenging and high-profile data science research projects in the broad area of data engineering, large-scale data analytics and distributed data systems. You will pro-actively represent the key requirements of the School of Informatics (which has critical research driven AI and Data Science needs) so that they have significant influence on the architectural and prioritisation decisions taken for the EIDF GPU platform going forward. You are also expected to liaise with key stakeholders from GAIL, the University of Edinburgh's newly founded Generative AI Laboratory, ensuring their data and modelling requirements are represented in the EIDF GPU cluster. Therefore, individuals who can effectively communicate and collaborate are particularly sought. You must be technically excellent with analysis, design and software engineering skills in C/C++, Java or Python. You will have a track-record of successfully completed projects and a high degree of analytical and intellectual curiosity. You will have expert understanding of job scheduling, resource sharing and prioritisation, using systems such as Kubernetes and Slurm, GPU’s, containerisation and distributed filesystems. In-depth knowledge of the current technology trends in “big data”, including frameworks for advanced analytics, machine learning and generative AI, is essential.

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