Machine Learning Engineer, VP

What you’ll do

Your daily responsibilities will include you collaborating with colleagues to design and develop advanced machine learning products which power our group for our customers. You’ll also codify and automate complex machine learning model productions, including pipeline optimisation.

We’ll expect you to transform advanced data science prototypes and apply machine learning algorithms and tools. You’ll also plan, manage, and deliver larger or complex projects, involving a variety of colleagues and teams across our business.

You’ll also be responsible for:

1. Understanding the complex requirements and needs of business stakeholders, developing good relationships and how machine learning solutions can support our business strategy
2. Working with colleagues to productionise machine learning models, including pipeline design and development and testing and deployment, so the original intent is carried over to production
3. Creating frameworks to ensure robust monitoring of machine learning models within a production environment, making sure they deliver quality and performance
4. Understanding and addressing any shortfalls, for instance, through retraining
5. Leading direct reports and wider teams in an Agile way within multi-disciplinary data and analytics teams to achieve agreed project and Scrum outcomes

The skills you’ll need

To be successful in this role, you’ll need to have a good academic background in a STEM discipline, such as Mathematics, Physics, Engineering or Computer Science. You’ll also have the ability to use data to solve business problems, from hypotheses through to resolution.

We’ll look to you to have twelve years of experience with machine learning on large datasets, as well as experience building, testing, supporting, and deploying advanced machine learning models into a production environment using modern CI/CD tools, including git, TeamCity and CodeDeploy.

You’ll also need:

1. Proficiency in Python and relevant libraries and Machine learing framework e.g. Tensorflow, Pytorch
2. Experience with AWS, Google Cloud Platform, or Azure for deploying machine learning models
3. Strong understanding of CI/CD pipelines, version control like Git and containerization like Docker and MLOps, LLMOps experience preferred
4. Knowledge of various machine learning algorithms and familiarity with concepts such as overfitting, and model evaluation metrics
5. Good communication skills with the ability to proactively engage with a wide range of stakeholders