Senior Product Manager - AI Personalisation & Recommendation

Role Mission

Lead the end-to-end productization of a future-proof, AI-native Growth Engine that dynamically orchestrates customer value interventions across acquisition, graduation churn prevention, cross-sell, upsell, and retention. This role is pivotal to Careem’s ambition to build self-optimizing, LLM-augmented growth loops integrated into our product fabric.

Key Responsibilities

• Architect and lead the product development of the Next Best Action Engine (NBA), integrating real-time signals, predictive models, reinforcement learning loops, and LLM-based personalization layers.
• Translate customer behavior insights into programmable customer journeys, stitched across product surfaces, CRM, and marketing automation platforms.
• Define infrastructure and experience-level requirements to enable autonomous treatment orchestration, including fallback logic, response explainability, and agentic task flows.
• Partner with ML/LLM teams to integrate hybrid AI stacks—combining scoring models, embedding-based retrieval, and agentic flows—to drive personalized actions at scale.
• Own the roadmap for end-to-end system integration into customer data platforms (CDPs), notification systems, experiment orchestration layers, and analytics observability pipelines.
• Collaborate closely with marketing, engineering, data science, and ops to scale interventions across acquisition, engagement, and monetization loops.

Ideal Candidate Profile

• 5+ years of product leadership experience, with a deep focus on AI-powered growth systems, personalization infrastructure, or lifecycle automation at scale.
• Experience leading the development of Next Best Action engines, personalization platforms, or autonomous lifecycle systems in B2C environments.
• Strong working knowledge of LLMs, retrieval pipelines, memory architecture, and prompt engineering principles.
• Proven experience integrating products with CDPs, mar-tech stacks (Braze, Iterable), real-time event buses, and segmentation frameworks.
• Experience leading cross-functional pods comprising ML/LLM engineers, infra architects, and marketing operators.
• Fluency in AI/ML workflows including propensity modeling, supervised learning, clustering, causal impact analysis, and LLM-agent design patterns.
• Strong belief in productized intelligence—AI systems that can autonomously adapt and drive growth outcomes without manual tuning.