As the Tech Lead – Search & Personalization, you will lead the development of recommendations, search relevance algorithms, and personalization models to improve product discovery and user experience on Buymed’s platform. You will be responsible for designing, optimizing, and scaling search infrastructure while ensuring accurate and efficient ranking, filtering, and recommendations. You will lead a cross-functional squad focused on continuous improvement, automation, and increasing the efficiency of search and personalization systems, making a direct impact on user engagement and transaction volume.

Key Responsibilities

• Search Optimization – Design and improve search ranking algorithms, query understanding, and relevance models to enhance product discovery.
• Recommendation Systems – Build and refine AI/ML-driven recommendations that drive conversions, increase order size, and improve user engagement.
• Personalization & AI – Develop dynamic, real-time personalization models based on user behavior, transaction history, and contextual data.
• Scalability & Performance – Optimize search indexing, ranking, and filtering for high performance and reliability across millions of products and users.
• Data-Driven Decision Making – Work with Data Science and Product teams to analyze search patterns, user behavior, and performance metrics to improve search and recommendation accuracy.
• A/B Testing & Experimentation – Implement and manage experimentation frameworks to test new ranking models, personalization strategies, and UI/UX improvements.
• Collaboration – Work closely with Product, UX, Engineering, and Business teams to align search and personalization logic with customer needs and business goals.
• Mentorship & Best Practices – Guide engineers in search infrastructure, ML models, and scalable system design while enforcing clean, maintainable, and efficient code.

Requirements

Must-Have:

• 5+ years of experience in software engineering, with at least 2 years in a Tech Lead role.
• Expertise in search technologies (Elasticsearch, OpenSearch, Solr, or Vespa) and their integration into large-scale platforms.
• Strong background in recommendation algorithms and personalization systems.
• Experience with ranking models, query parsing, vector search, and semantic search techniques.
• Proficiency in Python or Golang, with experience in building AI/ML-driven solutions.
• Solid experience with microservices architecture and distributed system patterns
• Strong system design skills for high-traffic, high-availability applications
• Proficient in concurrency handling
• Experience with big data processing (Spark, Kafka, Airflow) and real-time analytics.
• Proven ability to lead a squad focused on continuous improvement, automation, and increasing system efficiency in search and personalization.
• Strong problem-solving and analytical skills, with the ability to drive data-informed decision-making.

Nice-to-Have:

• Experience in eCommerce, B2B marketplaces, or healthcare platforms.
• Familiarity with Graph Neural Networks, Reinforcement Learning, or Deep Learning for search and recommendations.
• Experience implementing multi-modal search (text, image, voice search).
• Knowledge of A/B testing frameworks and experimentation strategies.
• Exposure to edge computing for AI inference and low-latency search processing.