ML Engineer

Machine Learning Development: Design, build and deploy machine learning models that solve practical business problems, including recommendation systems, demand forecasting, customer segmentation, churn prediction, pricing optimisation and fraud detection.

Production ML Systems: Build reliable and scalable machine learning services that deliver predictions in both real time and batch environments, ensuring strong performance, reliability and cost efficiency.

MLOps & Automation: Develop and maintain MLOps pipelines that automate model training, validation, deployment, monitoring and retraining.

Feature Engineering: Work with large datasets to create robust feature pipelines and reusable datasets that improve model performance and accelerate experimentation.

LLM & Generative AI Applications: Design, evaluate and deploy AI powered solutions using large language models (LLMs), retrieval systems, agents and emerging AI technologies to enhance customer experiences and internal productivity.

Model Monitoring & Optimisation: Implement monitoring frameworks to track model performance, drift, accuracy and business impact, continuously improving models in production.

Cross Functional Collaboration: Partner with Product Managers, Engineers, Analysts and business stakeholders to identify opportunities where machine learning can create measurable value.

Software Engineering Excellence: Develop solutions in line with software engineering best practices, including Git, CI/CD, trunk based development, testing and observability.

AI Collaboration: Contribute to experiments with AI and emerging technologies, helping shape how Kogan.com leverages machine learning and automation across the business.