Machine Learning Engineer – LLMs

Job title:

Machine Learning Engineer – LLMs

Company

Xcede

Job description

Machine Learning Engineer (LLMs)Up to £90,000 SalarySurrey office x2 days per monthOVERVIEWXcede’s Data-driven Insurance client with an excellent Data Science, ML Engineering and MLOps team is hiring for a Machine Learning Engineer to join their AI unit. The organisation have been building commercially successful Data Science products for years, and are now pushing further into Generative AI with 6 LLM based projects in production across the company.In this role you will be responsible for building and deploying production level ML models for a breadth of interesting commercial projects. The role is end to end focused. Your responsibilities as a Machine Learning Engineer will include but not be limited to:

• Developing and deploying predictive models and algorithms for commercial projects with a particular focus on LLMs and RAGs.
• Collaborate with cross-functional commercial and business teams, to understand business requirements and identify opportunities for deploying statistical models.
• Building production level Machine Learning models and be hands on in deployment.
• Evaluating and select appropriate machine learning techniques and algorithms for solving commercial challenges.

YOUR SKILLS & EXPERIENCEA successful Machine Learning Engineer will have the following:

• Have a number of years of commercial experience in a Data Science role building statistical & machine learning models.
• Have built and deployed machine learning models in production.
• Excellent use of Python
• Particular experience in NLP, LLMs, and RAGs are highly prized given the focus on these projects.
• CI/CD & MLOps experience is valued
• Databricks experience is valued

If this role interests you and you would like to find out more, please apply here or contact us via niall.wharton@Xcede.com (feel free to include a CV for review).

Expected salary

£80000 – 90000 per year

Location

Surrey

Job date

Thu, 18 Jul 2024 05:42:05 GMT

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