Architect and build scalable recommender systems end-to-end, from feature engineering and modeling to reliable production serving
Implement and integrate modern AI and LLM-based capabilities into scalable production systems
Write clean, maintainable, and testable production-quality code with a strong focus on reliability and long-term maintainability
Take full ownership of ML systems in production, including deployment, monitoring, performance optimisation, and system resilience
Enable controlled experimentation and continuous optimisation of recommender systems in production environments
Proactively experiment with new approaches, tools, and architectures to continuously improve recommender performance and system design
Collaborate closely with data scientists, software engineers, data engineers, and product managers to integrate ML solutions into scalable, production-ready system architectures
Continuously improve engineering standards, tooling, experimentation practices, and system robustness