I’m driving an organisation-wide push to personalise every digital touch-point and I need an AI/ML lead who can architect, prototype, and iterate a truly hybrid recommendation engine. Your core domain is Recommendation Technology—specifically the fusion of content-based and collaborative filtering—but you should also be comfortable pulling in signals from NLP pipelines, computer-vision models, or LLM embeddings whenever they strengthen relevance. Here’s the landscape you’ll step into: • We have product, behavioural, and metadata streams ready for feature engineering. • A scalable cloud stack (Python, TensorFlow/PyTorch, Spark, Kubernetes) is in place, waiting for a robust model layer. • Cross-functional teams are prepared to A/B test whatever you deploy. What I need from you: 1. A technical roadmap outlining algorithms, data needs, and evaluation metrics. 2. An initial proof-of-concept hybrid recommender that outperforms our current baseline across precision, recall, and diversity. 3. A production-grade implementation with automated retraining, monitoring, and explainability hooks. Acceptance criteria for phase one: • Offline validation shows ≥10 % lift in MAP over baseline. • Real-time inference latency under 150 ms at 99 th percentile. • Clear documentation of model architecture and feature pipeline. If you thrive on end-to-end ownership and enjoy collaborating closely with product and engineering, let’s talk and start shaping an experience our users will love.