Team Lead- Search & Personalisation

About the Role

An Engineering company is seeking a dynamic Team Lead – Search & Personalization to spearhead the entire Search & Personalization vertical. This role will involve managing a dedicated team focused on enhancing search systems in a high-scale consumer environment. With significant opportunities for professional growth, the successful candidate will directly impact user engagement and product intelligence.

Responsibilities

• Lead the end-to-end ownership of the Search & Personalization vision, including the technical roadmap and execution plan.
• Design and implement scalable search and information retrieval systems that yield highly relevant results for users.
• Enhance the search experience through advancements in query understanding, retrieval processes, ranking, re-ranking, and personalized content delivery.
• Steer the transition from traditional keyword-based search models to advanced semantic and AI-powered search methodologies.
• Utilize ML, NLP, embeddings, vector search, and LLM-based techniques to boost search relevance and user discovery.
• Establish and refine search relevance metrics, experimentation frameworks, and A/B testing procedures to validate improvements.
• Collaborate closely with Product, Data Science, and Engineering teams to address complex search and discovery challenges.
• Provide technical direction, mentorship, and leadership to a team of engineers while maintaining a hands-on approach to architecture and design.

Requirements

• 6–9 years of experience in software engineering with a focus on Search, Relevance, Information Retrieval, or Personalization.
• Proven experience in a Team Lead or technical leadership role, managing an extensive engineering charter.
• Strong foundational knowledge of Information Retrieval principles, including indexing, retrieval, ranking, and query understanding.
• Practical experience with leading search technologies such as Elasticsearch, OpenSearch, Solr, or Vespa.
• Demonstrated expertise in ranking strategies, learning-to-rank approaches, re-ranking techniques, and recommendation systems.
• Experience with semantic search, embeddings, vector search, and hybrid retrieval methodologies.
• Ability to apply machine learning, natural language processing, or large language models to search, discovery, or personalization challenges.
• Solid understanding of software engineering and distributed systems principles within a high-scale consumer product context.

Nice to Have

• +Familiarity with advanced data analytics to drive search improvements.
• +Experience in agile methodologies and product development cycles.
• +Knowledge of user experience design principles related to search interfaces.
• +Proficiency with cloud-based architectures and services.
• +Understanding of user engagement metrics and their impact on system design.