Machine Learning Platform Engineer | AWS, Kubernetes, MLOps

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Machine Learning Platform Engineer Sector: Financial Services Type: Permanent Working arrangement: Hybrid Salary: Up to £100,000.00 (DOE) Training a machine learning model is only part of the challenge. Deploying hundreds of models securely, reliably and consistently across a regulated enterprise requires a fundamentally different level of engineering. Cedar Technology is supporting a major financial services organisation investing in the next generation of its enterprise machine learning infrastructure. The business is developing a shared ML platform designed to give data scientists and machine learning engineers the tools, infrastructure and governance required to deploy models into production at scale. This is a platform engineering position with a genuine focus on ML workloads, rather than a conventional DevOps role with an AI label attached. The engineering challenge Build and evolve cloud-native ML infrastructure using AWS and Kubernetes. Develop scalable environments for model training, deployment and inference. Improve developer experience through automation, reusable tooling and self-service capabilities. Support orchestration and distributed workloads using technologies such as Kubeflow and Ray. Establish reliable CI/CD workflows, observability and deployment processes. Balance engineering productivity against the security, resilience and governance requirements of a regulated environment. What we’re looking for You’ll bring strong foundations in platform engineerin

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