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Lead Relation’s Platform Engineering team and technical strategy for hybrid on-premises and cloud infrastructure supporting machine learning, scientific computing, genomic pipelines, and internal services. Own Kubernetes architecture, MLOps environments, CI/CD, developer tooling, observability, disaster recovery, platform security, and governance. Hire and develop engineers, guide technical direction, manage production operations and incidents, and partner with Engineering, Data Science, and Machine Learning stakeholders.

Lead Relation's Data Platform team by designing a unified data strategy, optimizing data workflows, and ensuring data governance, while developing a high-performing engineering team.

Design, implement and optimise large neural network models and scalable training pipelines. Build distributed multi-GPU/multi-node training, profile and optimise GPU workloads, maintain core ML infrastructure, and productionise research models for downstream biological discovery workflows.

Develop and validate generative models of cellular behaviour using single-cell and perturbation data. Design models for counterfactual prediction and experimental design, collaborate with experimental teams to test hypotheses, evaluate models for causal and mechanistic fidelity, and communicate results across interdisciplinary stakeholders to accelerate therapeutic discovery.

As a Platform Engineer, develop and manage Kubernetes-based cloud infrastructure, enhance CI/CD pipelines, and collaborate with Machine Learning teams to optimize performance and reliability.