Key Responsibilities:

• Lead the productionalization of personalization and classification algorithms.
• Build with data and analytics in mind, to ensure continued accuracy and relevance of personalization, classification, and ranking algorithms.
• Translate experimental outputs (from AWS Sagemaker/Personalize) into production-ready systems, ensuring efficiency and reproducibility.
• Demonstrate full stack experience with specialization in backend or infrastructure while contributing to broader projects.
• Collaborate on architecture and technical decisions that influence the direction of the platform, ensuring scalability, performance, and user-focused improvements.
• Design and maintain model-serving infrastructure, including APIs, batch pipelines, or streaming systems required to deploy ML models reliably.
• Build with data and analytics in mind, to ensure continued accuracy and relevance of personalization and classification algorithms.
• Focus on performance optimization and system reliability, especially as our user base grows.
• Drive experimentation with MVPs, balancing rapid iteration with long-term sustainable growth and developer experience.
• Provide mentorship to engineers, fostering a culture of growth and collaboration.

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