Machine Learning Engineer Manager
- Lead the delivery of major Artificial Intelligence projects and help drive the next stage of Machine Learning development within Kingfisher.
- Oversee multiple teams working across a wide range of business domains and ensure they deliver complete end to end Machine Learning solutions.
- Develop and support Machine Learning team leads who each manage domain based project teams.
- Build and guide diverse teams that can translate business needs into Machine Learning solutions and communicate recommendations clearly to non technical stakeholders.
- Create an environment of cross team collaboration and knowledge sharing in order to improve effectiveness across Group AI.
- Work closely with Technology, Product and Data colleagues to help shape the data platforms that enable us to embed Machine Learning into our products and operational processes.
- Act as a visible advocate for the use of Machine Learning within Kingfisher and help promote its value across the group.
- Support the wider data leadership team in developing a strong data culture and in demonstrating the importance of data informed decisions.
- Contribute to the development and recognition of the Group AI brand both inside and outside the organisation
- Strong leadership experience with the ability to build, mentor and organise high performing Machine Learning teams.
- Excellent stakeholder management skills with the ability to understand real business needs and influence senior decision makers.
- Solid grounding in computer science including algorithms, data structures, modelling and software architecture.
- Strong understanding of classical Machine Learning and modern Deep Learning methods.
- Good proficiency in SQL and Python and familiarity with the Python data ecosystem.
- Experience working with Generative AI and agent based frameworks such as LangChain or LangGraph.
- Clear understanding of the Machine Learning development lifecycle and MLOps principles.
- Proven experience delivering AI solutions into production environments including cloud based services.
- Confidence working with CI/CD, data pipelines and containerised deployments such as Kubernetes and Kubeflow.
- Strong communication skills and the ability to manage several technical projects simultaneously.
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