Senior AI Engineer Computer Vision

Key Responsibilities - Design, develop, and deploy scalable machine learning systems that power demand forecasting and related AI products. - Design and maintain reliable ML workflows across the model lifecycle, including data preparation, experimentation, training, evaluation, deployment, inference, monitoring, and continuous improvement. - Build and optimise large-scale data processing and feature engineering pipelines using technologies such as PySpark and Databricks, enabling efficient preparation of training and inference datasets. - Develop multimodal machine learning solutions that combine diverse data sources, including product images, text, structured metadata, and behavioural signals, to create rich representations for downstream AI applications. - Train and productionise deep learning models using modern architectures such as Transformers, foundation models, and other representation learning approaches. - Partner with Applied Scientists to translate new modelling approaches into reliable, scalable production systems. - Improve the performance, reliability, scalability, and observability of ML systems operating in production. - Drive engineering excellence through architecture discussions, code reviews, mentoring, and knowledge sharing. About You You'll likely have: - Significant experience designing and deploying machine learning systems in production environments. - Strong software engineering skills in Python, with experience building maintainable, tested, and production-quality code. - Strong experience with large-scale data processing using technologies such as PySpark and Databricks. - Experience designing and building ML pipelines across the full lifecycle, from data preparation and model development through to deployment and monitoring. - Experience developing deep learning models using frameworks such as PyTorch, TensorFlow, or similar. - Experience with multimodal machine learning, representation learning, or embedding models, combining data sources such as images, text, structured metadata, or behavioural signals. - Strong understanding of modern deep learning architectures, particularly Transformers, foundation models, multimodal learning, and representation learning techniques. - Experience working with distributed computing, large datasets, and scalable model training or inference systems. - Familiarity with cloud platforms and modern MLOps practices. - Strong communication skills and the ability to collaborate effectively with scientists and engineers. - A pragmatic mindset, balancing technical excellence with delivering business value.