Principal Search & Recommendation Architect

Principal Search & Recommendation Architect to lead the strategy, architecture, and modernization of personalized digital shopping experiences across  and mobile platforms. This individual will serve as the technical leader responsible for search relevance, recommendation systems, customer personalization, and next-generation AI-powered product discovery capabilities.

The ideal candidate combines deep expertise in machine learning, search technologies, recommendation engines, data engineering, and cloud modernization. This role will define the roadmap for real-time personalization and customer engagement while partnering with product, engineering, data science, and executive leadership teams.

• Own enterprise search and recommendation architecture across digital commerce platforms.
• Lead development of personalized search experiences, product ranking algorithms, and recommendation strategies.
• Design and implement customer behavior models, customer segmentation frameworks, and next-best-action capabilities.
• Drive roadmap for:
• Personalized search rankings
• Recommendation engines
• Basket recommendations
• Loyalty personalization
• Digital coupon optimization
• Mobile app personalization
• Architect real-time ranking systems utilizing customer behavior signals and event streams.
• Build and scale semantic search and vector search solutions.
• Evaluate and integrate LLM-powered shopping experiences and conversational commerce capabilities.
• Partner with business stakeholders to improve:
• Conversion rates
• Basket size
• Customer engagement
• Retention
• Digital revenue growth
• Lead cloud modernization efforts including migration of ML workloads from AWS and Databricks environments toward GCP services.
• Lead semantic search, vector search, and LLM-enabled shopping initiatives
• Drive AWS to GCP modernization strategy
• Establish architecture standards for feature stores, MLOps processes, experimentation frameworks, and model governance.
• Mentor engineering and data science teams while influencing strategy across multiple organizations.

• 10+ years of experience in machine learning, search, recommendation systems, or personalization platforms.
• Proven background architecting large-scale customer-facing platforms.
• Deep experience with:
• Search relevance
• Product ranking models
• Recommendation systems
• Personalization platforms
• Customer analytics
• Strong software engineering and machine learning foundation.
• Experience building:
• Real-time recommendation platforms
• Search ranking systems
• Behavioral targeting solutions
• Customer propensity models
• Expertise with:
• Python
• Spark
• Databricks
• Kafka
• Distributed data platforms
• Experience designing experimentation frameworks and A/B testing strategies.
• Executive communication and stakeholder management experience.