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Machine Learning Engineer This role exists to support the business in utilizing Machine Learning applications to drive additional value for our customers by feeding into the delivery of strategic data science solutions whilst setting up resilient and future-proof ML infrastructure and engineering foundations. Designing and developing our ML Operations (MLOps) infrastructure and practices to support the effective transition of machine learning models and PoCs into production in a cost and operationally efficient manner. Monitoring, and maintaining the health of new and existing ML models post deployment; Working with business stakeholders to proactively raise awareness of any risks to the model(s) outputs and/or their interpretation, and usability. Implementing modern code development and MLOps strategies, in collaboration with Data Science and Data Engineering to support the proactive identification, targeting and resolution of any ML model(s) performance issues across their entire lifecycle. Developing operating procedures, work instructions and high-level designs of ML pipelines and architectures to support the effective documentation of our work. Working with third party partners and software providers to improve, implement and/or support the move to production of novel Machine Learning solutions. Raising awareness of best practices for MLOps; collaborating and knowledge sharing with individuals across the data and software teams, and wider business. Collaborating with Data Science, Data Engineering and Software Engineering teams to drive cross-function visibility and reduction of technical debt across the teams. Designing and implementing ML infrastructure to support the deployment and monitoring of ML models in production in an efficient and cost-effective manner. Developing an MLOps strategy aligned to best practice and existing DevOps practices in Gymshark. Developing a strong knowledge of all data sources and maintaining relations with data users/processors