AI and Data Systems Engineer (KTP Associate)

About the role

This is a great opportunity to apply advanced data engineering techniques and artificial intelligence tools to develop and deploy an obsolescence data integration platform for gas turbine components. You will be delivering this ambitious innovation project and play a key role delivering the future strategy of Siemens Energy Industrial Turbomachinery Ltd, working with them at their base in Lincoln.

You will lead a transformation project – a Knowledge Transfer Partnership (KTP) – to develop an advanced platform designed for predicting and tracking components obsolescence in gas turbines using AI-driven techniques. Collaborating closely with the team at Siemens Energy Industrial Turbomachinery in Lincoln, you will also work alongside an academic team from the Mechanical and Construction Engineering and Computer and Information Sciences departments at Northumbria University, who will provide support to meet the project aims.

The role is fixed term for a duration of 28 months.

The opportunities available to you:

• Leading and delivering a challenging and strategic R&D project that directly impacts the business
• Support from an academic team with specialist knowledge and experience relevant to the project
• Potential to be retained in a permanent position within the company at the end of the project
• £4,667 professional training and personal development budget
• Potential for performance-related salary increments
• Leadership and Management training from Ashorne Hill

For full details of qualifications, skills and experience required for the post, please review the role description.

About the team

Find out more about Siemens Energy Industrial Turbomachinery Ltd: www.siemens-energy.com/uk/en/home.html

Find out more about Mechanical and Construction Engineering and Computer and Information Sciences at Northumbria University:

www.northumbria.ac.uk/about-us/academic-departments/mechanical-and-construction-engineering/research

www.northumbria.ac.uk/about-us/academic-departments/computer-and-information-sciences/research

For informal enquiries please contact Dr Hamed Farokhi, Northumbria University: hamed.farokhi@northumbria.ac.uk

Application submissions

To apply for this vacancy please click 'Apply Now', and submit:

1. A 2-page CV, highlighting how your skills and experience meet the project criteria (set out in the person specification document).
2. A one-page cover letteroutlining relevant projects you have led or worked on.
3. A short video(max 3 minutes): This is your opportunity to tell us how your skills and experience are suitable for the role. For this project, you will be tasked with developing an AI-driven platform to predict and manage component obsolescence in gas turbines. You will work with technologies such as database design, large language models, data integration techniques, and machine learning forecasting models. We are looking for expertise in Python, experience with databases like SQL and data warehouses such as Snowflake, proficiency in data visualisation tools like Power BI/Tableau/etc, as well as experience with machine learning and large language models. Upload the video to your preferred cloud provider (Google Drive, OneDrive, Dropbox, etc.) and include the link to the video clearly in your CV or cover letter.

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